Search

Multivariate time series data (Mv-TSD) portray the evolving processes of the system(s) under examination in a multi-view manner. Factorization methods are salient for Mv-TSD analysis with the potentials of structural feature construction. However, research challenges remain in the derivation of factors due to scattered data distribution of Mv-TSD and intensive interferences/outliers embedded in the source data. The proposed Enhanced Bayesian Factorization approach (Enhanced-BF) addresses the challenges in three phases: (1) variant scale partitioning applies to Mv-TSD according to degree of amplitude and obtains the blocks of variant scales; (2) hierarchical Bayesian model for tensor factorization automatically derives the factors of each block with interferences suppressed; (3) Bayesian unification model merges those block factors to construct the final structural features. Enhanced-BF has been evaluated using a case study of brain data engineering with multivariate electroencephalogram (EEG). Experimental results indicate that the proposed method manifests robustness to the interferences and outperforms the counterparts in terms of operation efficiency and error when factorizing EEG tensor. Besides, Enhanced-BF excels in factorization-based analysis of ongoing autism spectrum disorder (ASD) EEG: 3 times speed-up in factorization and 87.35% accuracy in ASD discrimination. The latent factors can distinctly interpret the typical EEG characteristics of ASD subjects.

The adaptive neurofuzzy inference system (ANFIS) is a structured multioutput learning machine that has been successfully adopted in learning problems without noise or outliers. However, it does not work well for learning problems with noise or outliers. High-accuracy real-time forecasting of traffic flow is extremely difficult due to the effect of noise or outliers from complex traffic conditions. In this study, a novel probabilistic learning system, probabilistic regularized extreme learning machine combined with ANFIS (probabilistic R-ELANFIS), is proposed to capture the correlations among traffic flow data and, thereby, improve the accuracy of traffic flow forecasting. The new learning system adopts a fantastic objective function that minimizes both the mean and the variance of the model bias. The results from an experiment based on real-world traffic flow data showed that, compared with some kernel-based approaches, neural network approaches, and conventional ANFIS learning systems, the proposed probabilistic R-ELANFIS achieves competitive performance in terms of forecasting ability and generalizability.

Semisupervised learning has been widely applied to deep generative model such as variational autoencoder. However, there are still limited work in noise-robust semisupervised deep generative model where the noise exists in both of the data and the labels simultaneously, which are referred to as outliers and noisy labels or compound noise. In this article, we propose a novel noise-robust semisupervised deep generative model by jointly tackling the noisy labels and outliers in a unified robust semisupervised variational autoencoder randomized generative adversarial network (URSVAE-GAN). Typically, we consider the uncertainty of the information of the input data in order to enhance the robustness of the variational encoder toward the noisy data in our unified robust semisupervised variational autoencoder (URSVAE). Subsequently, in order to alleviate the detrimental effects of noisy labels, a denoising layer is integrated naturally into the semisupervised variational autoencoder so that the variational inference is conditioned on the corrected labels. Moreover, to enhance the robustness of the variational inference in the presence of outliers, the robust β-divergence measure is employed to derive the novel variational lower bound, which already achieves competitive performance. This further motivates the development of URSVAE-GAN that collapses the decoder of URSVAE and the generator of a robust semisupervised generative adversarial network into one unit. By applying the end-to-end denoising scheme in the joint optimization, the experimental results demonstrate the superiority of the proposed framework by the evaluating on image classification and face recognition tasks and comparing with the state-of-the-art approaches.

Partial multilabel learning (PML) aims to learn from training data, where each instance is associated with a set of candidate labels, among which only a part is correct. The common strategy to deal with such a problem is disambiguation, that is, identifying the ground-truth labels from the given candidate labels. However, the existing PML approaches always focus on leveraging the instance relationship to disambiguate the given noisy label space, while the potentially useful information in label space is not effectively explored. Meanwhile, the existence of noise and outliers in training data also makes the disambiguation operation less reliable, which inevitably decreases the robustness of the learned model. In this article, we propose a prior label knowledge regularized self-representation PML approach, called PAKS, where the self-representation scheme and prior label knowledge are jointly incorporated into a unified framework. Specifically, we introduce a self-representation model with a low-rank constraint, which aims to learn the subspace representations of distinct instances and explore the high-order underlying correlation among different instances. Meanwhile, we incorporate prior label knowledge into the above self-representation model, where the prior label knowledge is regarded as the complement of features to obtain an accurate self-representation matrix. The core of PAKS is to take advantage of the data membership preference, which is derived from the prior label knowledge, to purify the discovered membership of the data and accordingly obtain more representative feature subspace for model induction. Enormous experiments on both synthetic and real-world datasets show that our proposed approach can achieve superior or comparable performance to state-of-the-art approaches.

Varied edge bundling methods have been used to reduce visual clutter in parallel coordinates plots (PCP). However, existing edge-bundled PCP do not scale well for visual analysis of multidimensional big data and often overplot the bundles in the area near the axes. In this study, we propose a scalable lightweight bundling method to support visual analysis of multidimensional big data in PCP. It helps the users discover trends and detect outliers in the data by bundling the edges between each two adjacent axes independently. We integrate human judgments into the two-dimensional data binning by novel interactions to accelerate the clustering process of the data. We use the frequency-based representation to render the clusters as histogram-like bundles to reveal the distribution of the data and eliminate the overplotting of the bundles. Based on our method, we build a lightweight web-based visual analytics system for exploring multidimensional big data in PCP. The scalability analysis of our method shows that its clustering time increases linearly with the size of the data. Its rendering time is independent of the size of the data. We conduct two case studies and a user study to compare our method with classic PCP and two state-of-the-art edge-bundled PCP.

21 pages, 8 figures, 1 table, supporting information https://doi.org/10.1111/mec.16840.-- Data Availability Statement: Raw reads have been deposited to European Nucleotide Archive (ENA) under the accession id PRJEB45992. A VCF file with all filtered and unlinked SNPs used in the analyses of this study is provided as an additional file., By evaluating genetic variation across the entire genome, one can address existing questions in a novel way while raising new ones. The latter includes how different local environments influence adaptive and neutral genomic variation within and among populations, providing insights into local adaptation of natural populations and their responses to global change. Here, under a seascape genomic approach, ddRAD data of 4609 single nucleotide polymorphisms (SNPs) from 398 sardines (Sardina pilchardus) collected in 11 Mediterranean and one Atlantic site were generated. These were used along with oceanographic and ecological information to detect signals of adaptive divergence with gene flow across environmental gradients. The studied sardines constitute two clusters (FST = 0.07), a pattern attributed to outlier loci, highlighting putative local adaptation. The trend in the number of days with sea surface temperature above 19°C, a critical threshold for successful sardine spawning, was crucial at all levels of population structuring with implications on the species' key biological processes. Outliers link candidate SNPs to the region's environmental heterogeneity. Our findings provide evidence for a dynamic equilibrium in which population structure is maintained by physical and ecological factors under the opposing influences of migration and selection. This dynamic in a natural system warrants continuous monitoring under a seascape genomic approach that might benefit from a temporal and more detailed spatial dimension. Our results may contribute to complementary studies aimed at providing deeper insights into the mechanistic processes underlying population structuring. Those are key to understanding and predicting future changes and responses of this highly exploited species in the face of climate change, This study was carried out within the European Research Contract SPELMED (EASME/EMFF/2016/032) funded by the Executive Agency for Small- and Medium-sized Enterprises (EASME) of the European Commission (DGMARE). [...]. MC would like to acknowledge partial funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 869300 (FutureMARES). MC and FR acknowledge the institutional support of the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S). FR was supported by the Ramón y Cajal programme (Spanish Ministerio de Ciencia e Innovación, RYC2020-030078-I). This research was supported in part through computational resources provided by IMBBC (Institute of Marine Biology, Biotechnology and Aquaculture) of the HCMR (Hellenic Centre for Marine Research). Funding for establishing the IMBBC HPC has been received from the MARBIGEN (EU Regpot) project, LifeWatchGreece RI and the CMBR (Centre for the study and sustainable exploitation of Marine Biological Resources) RI, implemented under the Action “Reinforcement of the Research and Innovation Infrastructure,” funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014-2020) and cofinanced by Greece and the European Union (European Regional Development Fund)

The Micro-expressions (MEs) are very important nonverbal communication clues. However, due to their local and short nature, spotting them is challenging. In this paper, we address this problem by using a dedicated local and temporal pattern (LTP) of facial movement. This pattern has a specific shape (S-pattern) when ME are displayed. Thus, by using a classical classification algorithm (SVM), MEs are distinguished from other facial movements. We also propose a global final fusion analysis on the whole face to improve the distinction between ME (local) and head (global) movements. However, the learning of S-patterns is limited by the small number of ME databases and the low volume of ME samples. Hammerstein models (HMs) are known to be a good approximation of muscle movements. By approximating each S-pattern with a HM, we can both filter outliers and generate new similar S-patterns. By this way, we perform a data augmentation for S-pattern training dataset and improve the ability to differentiate micro-expressions from other facial movements. The spotting results, performed on CASME and CASME, show that our proposed LTP outperforms the most popular spotting method in terms of F1-score. Adding the fusion process and data augmentation improve even more the spotting performance.

Broad learning system (BLS) is a novel and efficient model, which facilitates representation learning and classification by concatenating feature nodes and enhancement nodes. In spite of the efficient properties, BLS is still suboptimal when facing with imbalance problem. Besides, outliers and noises in imbalanced data remain a challenge for BLS. To address the above issues, in this paper we firstly propose a weighted BLS, which assigns a weight to each training sample, and adopt a general weighting scheme, which augments the weight of samples from the minority class. To further explore the prior distribution of original data, we design a density based weight generation mechanism to guide the specific weight matrix generation and propose the adaptive weighted broad learning system (AWBLS). This mechanism considers the inter-class and intra-class distance simultaneously in the density calculation. Finally, we propose the incremental weighted ensemble broad learning system (IWEB) by utilizing a progressive mechanism to further improve the stability and robustness of AWBLS. Extensive comparative experiments on 30 real-world data sets verfy that IWEB outperforms most of the imbalance ensemble classification methods.

As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel. In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely, a linear combination of several zero-mean Gaussian kernels with different widths. In both correntropy and MC, the center of the kernel function is, however, always located at zero. In the present work, to further improve the learning performance, we propose the concept of multikernel correntropy (MKC), in which each component of the mixture Gaussian kernel can be centered at a different location. The properties of the MKC are investigated and an efficient approach is proposed to determine the free parameters in MKC. Experimental results show that the learning algorithms under the maximum MKC criterion (MMKCC) can outperform those under the original maximum correntropy criterion (MCC) and the maximum MC criterion (MMCC).

Multiview learning (MVL), which enhances the learners' performance by coordinating complementarity and consistency among different views, has attracted much attention. The multiview generalized eigenvalue proximal support vector machine (MvGSVM) is a recently proposed effective binary classification method, which introduces the concept of MVL into the classical generalized eigenvalue proximal support vector machine (GEPSVM). However, this approach cannot guarantee good classification performance and robustness yet. In this article, we develop multiview robust double-sided twin SVM (MvRDTSVM) with SVM-type problems, which introduces a set of double-sided constraints into the proposed model to promote classification performance. To improve the robustness of MvRDTSVM against outliers, we take L1-norm as the distance metric. Also, a fast version of MvRDTSVM (called MvFRDTSVM) is further presented. The reformulated problems are complex, and solving them are very challenging. As one of the main contributions of this article, we design two effective iterative algorithms to optimize the proposed nonconvex problems and then conduct theoretical analysis on the algorithms. The experimental results verify the effectiveness of our proposed methods.

Accurate and robust state of charge (SOC) estimation of lithium-ion batteries (LIBs) is very important to prolong battery life and prevent catastrophic failures. However, the accuracy of SOC estimation is seriously affected by unknown noise, uncertain interference and temperature. In this paper, a novel model fusion method is presented to achieve precise SOC estimation in the case of non-Gaussian noise and outliers. A state-space model of battery system is first developed to conduct SOC estimate. Then, a novel robust kernel Takagi Sugeno (T-S) fuzzy method to minimize the mean and variance of model error is developed to characterize the electrical performance of battery. This modeling strategy uses local nonlinear modeling mechanism, which the nonlinear relationship between data can be well represented, thus it can obtain a superior modeling performance. Finally, a multi-innovation unscented Kalman filter (UKF) algorithm considering the historical state information is designed to incorporate with the robust fuzzy model to filter out the noise in the observation and update the SOC estimation. Additional stability analysis shows the convergence of the proposed multi-innovation UKF algorithm. Experiments and verifications show that the presented estimation architecture is effective and has better modeling ability compared with several common methods.

Network data constantly evolves with new network applications and protocols. There is a need for robust techniques to detect anomalous behaviour. Offline models trained with static data lose validity when new variants of traffic emerge. They require retraining but the need for ground truth and lengthy training times make this task challenging. Meanwhile, online models which detect outliers in streaming data are susceptible to the curse of dimensionality and natural variability. Today's anomalies may be tomorrow's new traffic and existing methods do not provide a way to differentiate between them. We propose a framework that makes the most of both approaches: an offline deep learning model extracts features of normal traffic and provides a bias for an online outlier detection model to select data for training. The online model retains its previously learnt knowledge and retrains itself with new data. Online thresholds are updated in a drifting manner and the Mann-Whitney-U test is incorporated to prevent inaccurate updates. We perform analysis on the scores, develop heuristics to detect new traffic and evaluate using three deep learning models and four outlier detection methods on the UNSW-NB15 and CTU-13 datasets. The framework improves upon any individual offline or online models in isolation.

Matrix completion, in essence, involves recovering a low-rank matrix from a subset of its entries. Most existing methods for matrix completion neglect two significant issues. First, in several practical applications, such as collaborative filtering, some samples may be corrupted completely. However, most of the robust algorithms consider only the condition that a few components of each column have been arbitrarily contaminated. Second, many real data are not static in nature. Nevertheless, the conventional batch-based matrix completion methods cannot efficiently deal with the out-of-sample, that is, the vector completion problem. In this article, we first provide a novel robust matrix completion model and then develop an efficient optimization method that only requires conducting one time singular value decomposition for a thin matrix per iteration. Furthermore, by exploiting the essence of online matrix completion algorithms, we develop a vector completion model which can help users predict the missing values of out of sample. Numerical comparisons with traditional batch-based and online matrix completion algorithms demonstrate the benefits of the proposed method on streaming data corrupted by column outliers. Moreover, we show that our model can be used to detect outliers from incomplete information. This advantage is validated via numerous experimental results on synthetic and real-world data.

Virtualization brings the benefits of promoted utilization and scalability to the multi-tenant cloud platforms. However, the hypervisor, as one of the foundations in virtualization, is challenging to survive under various malicious attacks due to its large attack surface. This paper presents a novel group detection framework for the hypervisor code integrity, called Outlier. A large-scale cloud is split into individual Outlier detection groups. In an Outlier group, each host contains two parts. One is a distributed detection protocol, called Co-protocol, and the other is a detection interface, called Checker. The Co-protocol constructs trust for the integrity detection within an Outlier group. With the Co-protocol, each Checker conducts reliable integrity detection on the hypervisor code, and then the potential outlier host is perceived. We implement our Outlier prototype on the Xen hypervisor and evaluate its overhead. Experiments show that the introduction of the Outlier has few impacts on the performance of the virtualized systems.

A recommender system (RS) is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach becomes highly popular when implementing a RS. However, current LF models mostly adopt single distance-oriented Loss like an L₂ norm-oriented one, which ignores target data's characteristics described by other metrics like an L₁ norm-oriented one. To investigate this issue, this article proposes an L₁-and-L₂-norm-oriented LF (L³F) model. It adopts twofold ideas: 1) aggregating L₁ norm's robustness and L₂ norm's stability to form its Loss and 2) adaptively adjusting weights of L₁ and L₂ norms in its Loss. By doing so, it achieves fine aggregation effects with L₁ norm-oriented Loss's robustness and L₂ norm-oriented Loss's stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an L³F model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications.

Data-driven fault diagnosis primarily involves the identification of different fault locations and fault severities. Focusing on a chal-lenging task for which the target fault severities do not exist in the training samples, this study proposes a generalized cross-severity bearing fault diagnosis scheme based on a novel hierarchical cross-category inference framework. The proposed method uses an outlier detection scheme based on unsupervised feature mapping and local outlier probability calculation to identify the unseen samples. A neural network embedded with a tree-structured decision layer acts as a backbone to execute fault diagnosis at different hierarchies for different sample types, seen or unseen. Additionally, the metric learning method is used to support the approximate severity inference of the unseen samples after the fault locations are identified in the hierarchical model. Experiments performed on an aeronautical bearing test rig revealed that the proposed scheme is both feasible and superior to existing methods.

Community detection is an important area of research in social media mining. Numerous algorithms have been developed to detect disjoint, overlapping, and dynamic communities in a network. However, most of the existing algorithms do not consider the influence of each node in a group, which may be different and can affect the result of community detection. In this paper, we have proposed a novel Louvain-based algorithm named “NI-Louvain,” which considers the influence of each node in a group that not only detects community but also most influential nodes in the community. The proposed algorithm works in three steps. First, the input graph is processed to reduce its density. This is done by calculating cliques from the graph. In second step, Louvain's multilevel algorithm is applied to this clique graph. The resultant community obtained has a better modularity value than the communities obtained from existing algorithms like edge betweenness, label propagation, leading eigen, Louvain, fast greedy, walktrap, and infomap. In the third step, the nodes of resultant communities are transformed back to the nodes of the original graph. This step is beneficial in detecting overlapping communities, fuzzy membership of each node, most influential node in each community formed, and outlier nodes.

In medical image analysis (MIA) and computer-assisted surgery (CAS), aligning two multiple point sets (PSs) together is an essential but also a challenging problem. For example, rigidly aligning multiple point sets into one common coordinate frame is a prerequisite for statistical shape modelling (SSM). Accurately aligning the pre-operative space with the intra-operative space in CAS is very crucial to successful interventions. In this article, we formally formulate the multiple generalized point set registration problem (MGPSR) in a probabilistic manner, where both the positional and the normal vectors are used. The six-dimensional vectors consisting of both positional and normal vectors are called as generalized points. In the formulated model, all the generalized PSs to be registered are considered to be the realizations of underlying unknown hybrid mixture models (HMMs). By assuming the independence of the positional and orientational vectors (i.e., the normal vectors), the probability density function (PDF) of an observed generalized point is computed as the product of Gaussian and Fisher distributions. Furthermore, to consider the anisotropic noise in surgical navigation, the positional error is assumed to obey a multi-variate Gaussian distribution. Finally, registering PSs is formulated as a maximum likelihood (ML) problem, and solved under the expectation maximization (EM) technique. By using more enriched information (i.e., the normal vectors), our algorithm is more robust to outliers. By treating all PSs equally, our algorithm does not bias towards any PS. To validate the proposed approach, extensive experiments have been conducted on surface points extracted from CT images of (i) a human femur bone model; (ii) a human pelvis bone model. Results demonstrate our algorithm's high accuracy, robustness to noise and outliers.

Fault prognosis based on single model is generally inaccurate due to the varying working conditions. A multistage fault prognosis methodology combining stage identification with Bayesian networks (BNs) and time series approach with particular emphasis on the autoregressive moving average (ARMA) model is proposed to solve this problem. In the first stage, degradation data are identified, and outliers are marked by the Euclidean distance. Degenerate attributes of outliers are finely identified by BNs and matched to the corresponding model. In the second stage, the ARMA model is used for prognosis according to the results of the fine identification. Subsequently, the double-precision identification and ARMA submodel prognosis are carried out alternately throughout the prognosis process. Three degradation types of PMSM are simulated to verify the applicability of the method. Result shows that it can track the changes in the degradation in time and obtains better results.

lsub1/sub-norm quantile regression is a common choice if there exists outlier or heavy-tailed error in high-dimensional data sets. However, it is computationally expensive to solve this problem when the feature size of data is ultra high. As far as we know, existing screening rules can not speed up the computation of the lsub1/sub-norm quantile regression, which dues to the non-differentiability of the quantile function/pinball loss. In this paper, we introduce the dual circumscribed sphere technique and propose a novel lsub1/sub-norm quantile regression screening rule. Our rule is expressed as the closed-form function of given data and eliminates inactive features with a low computational cost. Numerical experiments on some simulation and real data sets show that this screening rule can be used to eliminate almost all inactive features. Moreover, this rule can help to reduce up to 23 times of computational time, compared with the computation without our screening rule.

We consider calculation of the mode of compositional data, the data related to each other through a linear constraint. Compositional data arises in various extensions of the fuzzy sets theory (type-2 interval-valued, intuitionistic, hesitant fuzzy sets), biomedicine (relative abundance, genome sequencing, activity recognition), and data analytics (various wealth indices, interval-valued observations, traffic congestion, etc.). Mode is a pre-aggregation function in the case of single variable, used as a classical estimator robust to outliers, but its multivariate extensions face the challenges of high computational complexity and potential oversmoothing. In this work we present several novel techniques for mode estimation on the unit k-simplex representing compositional, interval-valued, and general vector-valued data. We highlight the re-weighted k-nearest neighbours algorithm based on the Choquet integral with respect to a 2-additive fuzzy measure, compare its performance against other approaches based on spatial partitioning, and illustrate its applications to aggregation of real-world interval-valued data sets.

Outlier detection in multivariate data is one of the critical challenges in preprocessing phase. Many outlier detection methods have been emerged for the past few years to perform outlier detection efficiently in multivariate datasets. The prediction accuracy cannot be improved without proper outlier analysis and the prediction model might not confirm the expected behavior. The generation of huge data in real time applications makes the outlier detection process more crucial and challenging. Most of the currently available detection methods are based on mean and covariance that are not suitable for handling large volume of datasets, they are suitable for handlind static data and simple data to detect outliers. They cannot cope up with large scale data. So, there is a need for an efficient outlier detection model to detect the outliers in multivariate datasets. The primary objective of this research work is to develop a robust model for outlier detection in multivariate data. To achieve this, the work proposed an enhanced Hidden Semi-Markov Model (HSMM) which allows arbitrary time distribution in its states to detect outliers. The proposed work utilized six benchmark datasets and the performance is compared with several outlier detection algorithms such as HMM, iForest, FastABOD, and Expose. The work achieves 98.2 % of accuracy which is significantly better for detecting outliers in multivariate dataset. The proposed work improvised the percentage of acheivements between 2% to 25% than the currently available models.. The experimental analysis shows that the proposed model performs well than the currently available models in terms of accuracy, and receiver operation curve (ROC).

K-means is one of ten popular clustering algorithms. However, k-means performs poorly due to the presence of outliers in real datasets. Besides, a different distance metric makes a variation in data clustering accuracy. Improve the clustering accuracy of k-means is still an active topic among researchers of the data clustering community from outliers removal and distance metrics perspectives. Herein, a novel modification of the k-means algorithm is proposed based on Tukey’s rule in conjunction with a new distance metric. The standard Tukey rule is modified to remove the outliers adaptively by considering whether the data is distributed to the left, right or even to the input data's mean value. The elimination of outliers is applied in the proposed modification of the k-means before calculating the centroids to minimize the outliers' influences. Meanwhile, a new distance metric is proposed to assign each data point to the nearest cluster. In this research, the modified k-means significantly improves the clustering accuracy and centroids convergence. Moreover, the proposed distance metric's overall performance outperforms most of the literature distance metrics. This manuscript's presented work demonstrates the significance of the proposed technique to improve the overall clustering accuracy up to 80.57% on nine standard multivariate datasets.

The measurement error evaluation of Power Metering Equipment (PME) is significant for the instrument design and accurate metering of electric energy, especially in extreme environmental stresses. However, actual measurement error prediction is often disturbed by environmental noise and insufficient feature information. To address this problem, an Improved Local Outlier Factor (ILOF) method is first proposed to detect potential outliers. And an optimized distance function and adaptive threshold constraint method based on boxplot are used to improve the outlier detection performance of ILOF. Next, an error prediction method, namely Kernel Support Vector Regression (KSVR), is presented to fuse measurement error and multiple extreme environmental stresses by using the proposed kernel approach. Integrating the ILOF and KSVR, examples from the extreme environmental region demonstrate that the proposed evaluation framework has a higher assessment performance. Compared with several state-of-art prediction methods, our framework has profound outlier identification and error prediction performance under small sample conditions.

Wireless Sensor Networks (WSNs) are developed significantly from the last decades and attracted the attention of scientific and industrial domains. In WSNs, sensor nodes distributed autonomously in harsh environments are easily vulnerable to faults and attacks that cause sensor readings unreliable and inaccurate. In this scenario, sensor readings that have differed considerably from healthy behaviors will be considered abnormal data or anomalies/outliers. The inclusion of such outliers in data analytics will inevitably affect the outcome of the decision-making process. Thus, detecting outliers in WSNs using data-driven approaches becomes a novel technique among the Machine Learning (ML) communities. Meanwhile, various research issues are there in measuring the performance of the deployed ML algorithms in detecting outliers in WSNs, which generally contains minimum resources in terms of computational capability and power sources to ensure data quality. Hence, this paper presents a comprehensive overview of the state-of-the-art Statistical and Artificial Intelligence (AI) based techniques used in WSNs to detect outliers in the view of architecture, type, degree, approach, and detection mode. Furthermore, each aforementioned outlier detection approach is presented with detailed discussions and future scope for developments.

Robotic 3D measurement for evaluating the final shape of blades can improve product quality and reduce fabrication costs. However, large or complex objects require multiple scans from different viewpoints to capture the entire object. Errors in registration artificially introduce defects and surface discontinuties in the reconstructed object model. To address the sources of inaccurate registration for methods relying on pairwise associated points, this manuscript describes an inlier selection approach based on the geometric properties of rigid registration, called RiGID. It readily applies to registration strategies that use 3D interest points and descriptors to associate data across overlapping point clouds. RiGID converts geometric constraint violations into distributions over geometric error values. Analysis of the distributions provides a deterministic and data adaptive way to identify inlier sets. RiGID is an efficient preprocessor to rigid registration. It reduces the amount of input data and improves the inlier ratio, which translates to more efficient and accurate registration. Evaluation of the method shows a higher inlier rate and lower registration error compared to baseline outlier rejection methods. The approach is well suited to surfaces with smooth regions and a high percentage of outliers (up to 90\%), which engine and turbine blades exhibit.

Breast cancer is the second most common cancer type and is the leading cause of cancer-related deaths worldwide. Since it is a heterogeneous disease, subtyping breast cancer plays an important role in performing a specific treatment. Gene expression data is a viable alternative to be employed on cancer subtype classification, as they represent the state of a cell at the molecular level, but generally has a relatively small number of samples compared to a large number of genes. Gene selection is a promising approach that addresses this uneven high-dimensional matrix of genes versus samples and plays an important role in the development of efficient cancer subtype classification. In this work, an innovative outlier-based gene selection (OGS) method is proposed to select relevant genes for efficiently and effectively classify breast cancer subtypes. Experiments show that our strategy presents an F1 score of 1.0 for basal and 0.86 for her 2, the two subtypes with the worst prognoses, respectively. Compared to other methods, our proposed method outperforms in the F1 score using 80% less genes. In general, our method selects only a few highly relevant genes, speeding up the classification, and significantly improving the classifier's performance.

This article explores the problem of semisupervised affinity matrix learning, that is, learning an affinity matrix of data samples under the supervision of a small number of pairwise constraints (PCs). By observing that both the matrix encoding PCs, called pairwise constraint matrix (PCM) and the empirically constructed affinity matrix (EAM), express the similarity between samples, we assume that both of them are generated from a latent affinity matrix (LAM) that can depict the ideal pairwise relation between samples. Specifically, the PCM can be thought of as a partial observation of the LAM, while the EAM is a fully observed one but corrupted with noise/outliers. To this end, we innovatively cast the semisupervised affinity matrix learning as the recovery of the LAM guided by the PCM and EAM, which is technically formulated as a convex optimization problem. We also provide an efficient algorithm for solving the resulting model numerically. Extensive experiments on benchmark datasets demonstrate the significant superiority of our method over state-of-the-art ones when used for constrained clustering and dimensionality reduction. The code is publicly available at https://github.com/jyh-learning/LAM.

Margin is an important concept in machine learning; theoretical analyses further reveal that the distribution of margin plays a more critical role than the minimum margin in generalization power. Recently, several approaches have achieved performance breakthroughs by optimizing the margin distribution, but their computational cost, which is usually higher than before, still hinders them to be widely applied. In this article, we propose margin distribution analysis (MDA), which optimizes the margin distribution more simply by maximizing the margin mean and minimizing the margin variance simultaneously. MDA is efficient and resistive to class-imbalance naturally, since its objective distinguishes the margin means of different classes and can be broken up into two linear equations. In practice, it can also cooperate with other frameworks such as reweight-minimization when facing complex circumstances with noise and outliers. Empirical studies validate the superiority of MDA in real-world data sets, and demonstrate that simple approaches can also perform competitively by optimizing margin distribution.

Anomaly (outlier) detection is one of the most important problems of modern data analysis. The sources of anomalies are varying. They can be the results of database users' mistakes, operational errors or just missing values. The problem is very important because of the fast growth of large data sets. Therefore, in this study, we present detailed results of work on the concept of Granular Computing-based approach to anomaly detection, classification, and gradation. The aim of the study is to introduce an innovative solution that allows the use of information granules to identify and classify anomalies. The novelty of the proposed solution consists in the use of fuzzy semantics implied by the statistical properties of the data considered. Moreover, instead of the classic approach to detecting anomalies in the data, it is proposed to determine the degree of anomaly for the data transformed to the new resulting state space. Thanks to the use of an innovative approach using the universal descriptor space, it is possible to determine the degree of anomaly, and by using various aggregation methods one can also specify its type.

Outlier detection is widely used in many fields, such as intrusion detection, credit card fraud detection, medical diagnosis, and so on. Existing outlier detection algorithms are mostly designed for dealing with numeric or categorical attributes. However, data usually exist in the form of mixed attributes in real-world applications. In this article, we propose a novel mixed attribute outlier detection method based on multigranulation relative entropy by employing the neighborhood rough set. First, the neighborhood system is constructed by optimizing the mixed distance metric and the radius of the statistical value. Second, the neighborhood entropy is introduced as an uncertainty measure of data. Furthermore, the three kinds of multigranulation relative entropy-based matrices are defined by three kinds of attribute sequences, and the multigranulation relative entropy-based outlier factor is integrated to indicate the outlier degree of every object. Based on the proposed outlier detection model, the corresponding algorithm is designed. Finally, the proposed algorithm is compared with other nine algorithms through experiments on public data. The experimental results show that the proposed technique is adaptive and effective.

A milling cutter is one of the most important parts of machine tools. Its working status significantly influences the precision of workpieces. Due to the complex wear mechanism, the single sensor may be difficult to acquire the complete degradation information of milling cutters. Therefore, a feature learning based method is proposed to automatically extract features from multi-source data and predict the remaining useful life of cutting tools in real-time. Firstly, a statistic-based method is constructed to detect and delete the outliers hidden in the monitoring data. Secondly, the clean data is input into a multi-scale convolutional attention network (MSAN) to learn features and fuse multi-source data. At last, the fused data is used to predict the remaining useful life of cutting tools in a regression layer. Compared with traditional tool life prediction methods, the proposed method is able to fuse multi-source data through an attention feature learning model to conduct the life prediction of tools. Additionally, the data cleaning and model optimization methods are also proposed to promote engineering practicability. In order to validate the effectiveness of such method, the life testing experiments on milling cutters are conducted to obtain run-to-failure data. In those experiments, multi-sensor monitor data are acquired, which are used to conduct validation experiments testing the effectiveness of the proposed method. The results indicate the superiority of the proposed method in remaining useful life prediction milling cutters.

The development of industrial cyber-physical system (ICPS) provides a tight connection between the digital model and the industrial physical plant, which enables to use the data-driven methods to reflect the state of process running in the real world. However, due to the noisy and harsh industrial environment, the collected data are often corrupted to some extent. If the corrupted data are not detected in time, the data-driven model will inevitably degenerate and induce a poor process monitoring performance. In addition, the nonlinear characteristics between process variables due to the high complexities in physical plant bring challenges to the data-driven methods. In this article, a robust kernel dictionary learning method, which can overcome the negative influence of outliers and simultaneously extracts the nonlinear characteristics of industrial process, is proposed to address the above problems in ICPS. Our extensive experiments demonstrate that the proposed method has achieved significantly better and stable performance to deal with outlier detection and process monitoring in ICPS.

The problem of representative selection amounts to sampling few informative exemplars from large datasets. Existing approaches to data selection often fall short of simultaneously handling non-linear data structures, sampling concise and non-redundant subsets, rejecting outliers, and yielding interpretable outcomes. This paper presents a novel representative selection approach, dubbed MOSAIC, for drawing descriptive sketches of arbitrary manifold structures. Resting upon a novel quadratic formulation, MOSAIC advances a multi-criteria selection approach that maximizes the global representation power of the sampled subset, ensures novelty of the samples by minimizing redundancy, and rejects disruptive information by effectively detecting outliers. Theoretical analyses shed light on geometrical characterization of the obtained sketch and reveal that the sampled representatives maximize a well-defined notion of data coverage in a transformed space. In addition, we present a highly scalable randomized implementation of the proposed algorithm shown to bring about substantial speedups. MOSAIC's superiority in achieving the desired characteristics of a representative subset all at once while exhibiting remarkable robustness to various outlier types is demonstrated via extensive experiments conducted on both real and synthetic data with comparisons to state-of-the-art algorithms.

Extracting genes involved in cancer lesions from gene expression data is critical for cancer research and drug development. the method of feature selection has attracted much attention in the field of bioinformatics. Principal Component Analysis (PCA) is a widely used method for learning low-dimensional representation. Some variants of PCA have been proposed to improve the robustness and sparsity of the algorithm. However, the existing methods ignore the high-order relationships between data. In this paper, a new model named Robust Principal Component Analysis via Hypergraph Regularization (HRPCA) is proposed. In detail, HRPCA utilizes L2,1-norm to reduce the effect of outliers and make data sufficiently row-sparse. And the Hypergraph Regularization is introduced to consider the complex relationship between data. Important information hidden in the data are mined, and this method ensures the accuracy of the resulting data relationship information. Extensive experiments on multi-view biological data demonstrate that the feasible and effective of the proposed approach.

Cyberattacks in power systems that alter the input data of a load forecasting model have serious, potentially devastating consequences. Existing cyberattack-resilient work focuses mainly on enhancing attack detection. Although some outliers can be easily identified, more carefully designed attacks can escape detection and impact load forecasting. Here, a cyberattack-resilient load forecasting approach based on an adaptive robust regression method is proposed, where the observations are trimmed based on their residuals and the proportion of the trim is adaptively determined by an estimation of the contaminated data proportion. An extensive comparison study shows that the proposed method outperforms the standard robust regression in various settings.

Many research areas depend on group anomaly detection. The use of group anomaly detection can maintain and provide security and privacy to the data involved. This research attempts to solve the deficiency of the existing literature in outlier detection thus a novel hybrid framework to identify group anomaly detection from sequence data is proposed in this paper. It proposes two approaches for efficiently solving this problem: i) Hybrid Data Mining-based algorithm, consists of three main phases: first, the clustering algorithm is applied to derive the micro-clusters. Second, the kNN algorithm is applied to each micro-cluster to calculate the candidates of the group's outliers. Third, a pattern mining framework gets applied to the candidates of the group's outliers as a pruning strategy, to generate the groups of outliers, and ii) a GPU-based approach is presented, which benefits from the massively GPU computing to boost the runtime of the hybrid data mining-based algorithm. Extensive experiments were conducted to show the advantages of different sequence databases of our proposed model. Results clearly show the efficiency of a GPU direction when directly compared to a sequential approach by reaching a speedup of 451. In addition, both approaches outperform the baseline methods for group detection.

Modern metro systems in big cities have accumulated large amounts of passenger transit transaction data via the deployment of automatic fare collection (AFC) devices, which could facilitate a thorough analysis of passenger flow dynamics. To discover the underlying passenger flow patterns over all stations in an entire metro system, this paper proposes a multivariate functional principal component analysis (MFPCA) method. The functional integrity of a single daily passenger flow profile at each station is explicitly utilized, and the complex correlation among a multitude of daily passenger flow profiles from all stations is comprehensively modeled. Moreover, to simultaneously improve the interpretability of eigenvectors and mitigate the sensitivity to outliers, the MFPCA is formulated as a minimization problem with both a sparsity and a robustness penalty term. A computationally efficient algorithm is developed accordingly to obtain the eigenvectors. The superiority of our proposed sparse and robust MFPCA (SRMFPCA) is validated using a Hong Kong Mass Transit Railway (MTR) dataset. The derived sparse and smooth eigenvectors can be well interpreted as empirically meaningful passenger flow patterns. The results of our method can be further used as solid foundations for station clustering, correlation analysis and outlier identification.

In one-class classification, the low variance directions in the training data carry crucial information to build a good model of the target class. Boundary-based methods like One-Class Support Vector Machine (OSVM) preferentially separates the data from outliers along the large variance directions. On the other hand, retaining only the low variance directions can result in sacrificing some initial properties of the original data and is not desirable, specially in case of limited training samples. This paper introduces a Covariance-guided One-Class Support Vector Machine (COSVM) classification method which emphasizes the low variance projectional directions of the training data without compromising any important characteristics. COSVM improves upon the OSVM method by controlling the direction of the separating hyperplane through incorporation of the estimated covariance matrix from the training data. Our proposed method is a convex optimization problem resulting in one global optimum solution which can be solved efficiently with the help of existing numerical methods. The method also keeps the principal structure of the OSVM method intact, and can be implemented easily with the existing OSVM libraries. Comparative experimental results with contemporary one-class classifiers on numerous artificial and benchmark datasets demonstrate that our method results in significantly better classification performance. HighlightsThe low-variance directions are crucial for one-class classification (OCC).A new method of OCC emphasizing the low-variance directions is proposed.The method incorporates covariance information into convex optimization problem.Can be implemented and solved efficiently with existing software.Comparative experiments with contemporary classifiers show positive results.

Big data is the compilation of enormous data that arrives from diverse sources for instance online transaction details, social media, sensor data, etc. Such assortment of enormous data developed is tough to evaluate by conventional processing relevance’s. By the development and upcoming latent in the healthcare business field, it is essential to analyze an enormous noisy data to get significant information. In healthcare system, the aim of this work is to evaluate the medical database of diabetes patients by a mixture of innovative hierarchical decision attention network, association rules (AR) and multiclass outlier classification with MapReduce framework. The association rule apriori algorithm in a MapReduce framework considers health data to create regulations. This is employed to discover the association among disease and their signs. This examination is made by means of UCI machine learning datasets of diabetes containing 50 attributes. The results of the proposed algorithm are offered by parameters for instance precision, accuracy, recall, and F-score.

The evaluation of pilot brain activity is very important for flight safety. This study proposes a Hidden semi-Markov Model with Hierarchical prior to detect brain activity under different flight tasks. A dynamic student mixture model is proposed to detect the outlier of emission probability of HSMM. Instantaneous spectrum features are also extracted from EEG signals. Compared with other latent variable models, the proposed model shows excellent performance for the automatic inference of brain cognitive activity of pilots. The results indicate that the consideration of hierarchical model and the emission probability with t mixture model improves the recognition performance for Pilots' fatigue cognitive level.

This article develops a robust global strategy for identifying the linear parameter varying (LPV) errors-in-variables (EIVs) systems subjected to randomly missing observations and outliers. The parameter interpolated LPV autoregressive exogenous model with an uncertain/noisy input is investigated and a nonlinear state-space model is considered for the input generation model (IGM). The parameters estimation of the LPV EIV systems with nonideal observations is realized using the expectation-maximization algorithm which is particular effective for the incomplete data issue. To ensure the robustness in the identification, the Student's t-distribution which is characterized by its adjustable degree of freedom, is used to handle the measurement non-normality. Since the posterior distributions of the latent states in the IGM are also involved in the identification process and they are difficult to calculate directly, the particle filter is introduced to recursively approximate them instead. Finally, the verification examples are given to demonstrate the effectiveness of the developed strategy.

The research of semantic segmentation based on unsupervised domain adaptation greatly alleviates the high-cost bottleneck of manual annotation in deep learning. Inevitably domain gap limits the ability of target domain to learn knowledge from source domain. Previous domain alignment strategies aim to explore maximum domain-invariant space to enlarge knowledge that target domain learns. They based on the assumption that images are symmetrical in categories covering all predefined categories. However, in practical training, there is class asymmetry in image samples inter domains. This will lead to outlier categories and negative transfer, because it lacks the guidance of the corresponding category of the source domain. To tackle this issue, we propose a partial domain adaptive method on semantic segmentation to guide target model to learn category-level knowledge selectively. PAM (Partial Adaptive Map) module is introduced to motivate the target model to obtain more knowledge from non-outliers and less knowledge from outliers to avoid negative transfer. We further analyze the effectiveness of this method from the perspective of JS divergence. Our method has achieved significant improvement without additional discriminators. Experiments on general datasets GTA5 and SYNTHIA can compare with SOTA methods.

Accurate gaze estimation that frees from glints and the slippage problem is challenging. Pupil-contour-based gaze estimation methods can meet this challenge, except that the gaze accuracy is low due to neglecting the pupil's corneal refrac-tion. This paper proposes a refraction-aware gaze estimation approach using the real pupil axis, which is calculated from the virtual pupil image based on the derived function between the real pupil and the refracted virtual pupil. We present a 2D gaze estimation method that regresses the real pupil normal's spherical coordinates to the gaze point. The noise and outliers of calibration data are removed by aggregation filtering and Random Sample Consensus, respectively. Moreover, we propose a 3D gaze estimation method that transforms the real pupil axis to the gaze direction. Experimental results show that the proposed gaze estimation approach has comparable accuracy to state-of-the-art pupil-center-based gaze estimation methods, which suffer from the slippage problem.

Localization is fundamental for autonomous vehicle applications. Compared with widely developed LiDAR-based vehicle localization, vision based localization has attracted considerable attention in recent years owing to the low-cost sensor. One of the challenges for visual localization is the outlier in measurements due to the appearance changes caused by illumination, season, and weather. To address this problem, we present a real-time robust visual localization approach that achieves deterministic optimality with global convergence. The idea is to decouple the rotation and translation estimation by utilizing the fact that the pitch and roll angles of the query pose are similar to those of the map reference pose, since the vehicle motion is locally planar. Based on the decoupled formulation, we first estimate the optimal yaw angle and eliminate the majority of outliers by an efficient inlier voting method, then find the optimal translation by maximum clique search. The two subproblem estimators are embedded into a prioritized search paradigm to guarantee deterministic optimality. In the experiments, the simulation demonstrates that the proposed method can achieve superior robustness even dealing with extreme outlier rates (95%). Results on both public and self-collected real-world vehicle datasets validate the effectiveness of the proposed method in the real application.

Epipolar geometry is a fundamental constraint used in computer vision systems to estimate parameters using correspondence. The most common way to describe epipolar geometry is by means of a 3x3 matrix called the fundamental matrix, and such matrices are used to store the precise geometric information relating a pair of stereo images. Its efficient estimation substantially improves initialization in visual simultaneous localization and mapping (vSLAM), which uses correspondence-based epipolar geometry to determine the trajectory of the camera and a three-dimensional scene. Conventional robust methods for epipolar geometry estimation can become computationally inefficient and inaccurate when there are low-quality correspondences. Because semantic information can be more stable than pixel intensities/descriptors, a novel Faster Region-based Convolutional Network (R-CNN) learning-based approach called the semantic filter is proposed in this paper to address these problems. The semantic filter is first trained on different semantic patches, which are described in terms of their different outlier distributions, providing different semantic labels for image contexts. Then, the patches with low-level semantic labels are filtered out. Finally, precise and robust correspondences can be determined by matches using the high-level semantic contexts, making the correspondence-based calculation more accurate. For dynamic outdoor scenes, the results of extensive experiments show that our semantic filter can help vSLAM localize accurately and robustly on a map from different viewpoints. In a completely static scenario, our semantic filter can remove the low-quality correspondences, enabling the mobile robot to operate well.

Existing domain adaptation (DA) methods generally assume that different domains have identical label space, and the training data are only sampled from a single domain. This unrealistic assumption is quite restricted for real-world applications, since it neglects the more practical scenario, where the source domain can contain the categories that are not shared by the target domain, and the training data can be collected from multiple modalities. In this article, we address a more difficult but practical problem, which recognizes RGB images through training on RGB-D data under the label space inequality scenario. There are three challenges in this task: 1) source and target domains are affected by the domain mismatch issue, which results in that the trained models perform imperfectly on the test data; 2) depth images are absent in the target domain (e.g., target images are captured by smartphones), when the source domain contains both the RGB and depth data. It makes the ordinary visual recognition approaches hardly applied to this task; and 3) in the real world, the source and target domains always have different numbers of categories, which would result in a negative transfer bottleneck being more prominent. Toward tackling the above challenges, we formulate a deep model, called visual-depth matching network (VDMN), where two new modules and a matching component can be trained in an end-to-end fashion jointly to identify the common and outlier categories effectively. The significance of VDMN is that it can take advantage of depth information and handle the domain distribution mismatch under label inequality simultaneously. The experimental results reveal that VDMN exceeds the state-of-the-art performance on various DA datasets, especially under the label inequality scenario.

With the increased proliferation of smart devices, the transit passengers of today expect a higher quality of service in the form of real-time traffic updates, accurate expected time-of-arrival (ETA) predictions. Providing these services requires public transit agencies and private transportation players to maintain full situational awareness of the city-wide traffic. However, most such agencies and companies are resource-constrained and do not have access to city-wide traffic data. The availability of sparsely sampled and outlier-corrupted traffic data renders the resulting traffic maps patchy and unreliable and necessitates the use of sophisticated real-time traffic interpolation and prediction algorithms. Moreover, since the traffic data is measured and collected in a sequential manner, the estimations must also be generated online. Thankfully, the traffic matrices are spatially and temporally structured, allowing the use of time-series and matrix/tensor completion algorithms. This work puts forth a generative model for the traffic density and subsequently uses a variational Bayesian formalism to learn the parameters of the model. Specifically, we consider low-rank traffic matrices whose subspace evolves according to a state-space model with possible sparse outliers. Unlike most matrix/tensor completion algorithms, the proposed model is equipped with automatic relevance determination priors that allow it to learn the parameters in an entirely data-driven manner. A forward-backward algorithm is proposed that enables the updates to be carried out at low-complexity. Simulations carried out on real traffic speed data demonstrate that the proposed algorithm better predicts the future traffic densities as compared to the state-of-the-art matrix/tensor completion algorithms.

Robustness enhancement and feature selection are the two crucial issues to be resolved in robust palmprint recognition. However, existing regression-based methods are insufficient to handle outliers and select significant features. From a statistical viewpoint, we present a general framework to intrinsically resolve the two issues. By investigating the role of outliers in the formation of coding errors, we devise a double-Laplacian mixture-error model to faithfully fit the error distribution. Additionally, we design a supervised group-sparse regularizer to enforce the locality and group sparsity of the codes. Integrating the two parts into the framework produces a nonconvex constrained problem, for which we develop an iteratively reweighted l1-l1 minimization algorithm by combining the majorization-minimization strategy and the alternating direction method of multipliers. The weighted vector learned from the error model and the local group sparsity enforced by the regularizer enable our method to better handle outliers and select more significant features than the state-of-the-art methods. Extensive experimental results verify the flexibility and robustness of our method to various contaminations.

Initial alignment is of vital importance to the inertial navigation system, and the in-motion initial alignment is a critical stage for the moving vehicles. In this paper, an in-motion alignment method for SINS/GPS integration is investigated. Using the velocity, which is obtained from GPS, the observation and the reference vectors are constructed. Different from previous methods, a robust attitude determination method is devised to finish the in-motion alignment process when there are outliers contained in the GPS outputs. To implement the robust attitude determination method, the error of the magnitudes of the observation and reference vectors is calculated, and this error is used to detect the outliers. However, the bias of the inertial sensors, the expectation and variance of the calculated error of the magnitude is time-varying, so that the detected accuracy of the outliers degrades. To address this defect, the new normalized error of the magnitude is constructed by a robust parameter identification method, then the expectation and variance of the new normalized error are constant. Using the new normalized error, the initial attitude is obtained by the robust optimization-based alignment (ROBA) method. The simulation and field tests are designed to validate the performance of the proposed method, the alignment results show that the proposed method can detect the outliers accurately, and it gets the similar alignment accuracy with the current popular method when there are no outliers in the auxiliary velocity of GPS.