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Partial label learning (PLL) handles data classification problems by assigning a candidate label set to each sample. There is always one correct label in a candidate label set. Since the PLL can achieve classification without precise labels, it can reduce the cost of data annotation. However, the PLL suffers from challenges and difficulties caused by the ambiguity of the candidate labels. Most PLL algorithms eliminate the ambiguity in candidate label sets by treating each candidate label equally or iteratively identifying the ground-truth label without employing optimization kernels, whereas the optimal kernel can ensure better performance for PLL tasks. Moreover, there is no general framework to handle heterogeneous data classification in various applications. Inspired by the successful application of multi-kernel learning in machine learning, this paper integrates multi-kernel learning into the PLL framework to develop a new multi-kernel PLL (PL-MKL) algorithm, which adopts different kernels to map the original sample attributes to distinct nonlinear feature spaces. Accordingly, the model's classification performance can be enhanced by joining the mapping capabilities of multiple feature spaces while fully exploring the intrinsic distribution of data. Furthermore, the PL-MKL combines similarity and dispersion graphs to develop an innovative method based on graph contrast disambiguation. This approach maintains the manifold characteristics of the data and reflects the differences in candidate labels, thus alleviating intra-class differences while gaining inter-class ones. An efficient optimization algorithm is proposed to attain the underlined objectives. Extensive experiments demonstrate the competitive or superior performance of the suggested PL-MKL over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Energy consumption forecasting plays an important role in energy management, conservation, and optimization in manufacturing companies. Aiming at the tin smelting process with multiple types of energy consumption and a strong coupling with energy consumption, the traditional prediction model cannot be applied to the multi-output problem. Moreover, the data collection frequency of different processes is inconsistent, resulting in few effective data samples and strong nonlinearity. In this paper, we propose a multi-kernel multi-output support vector regression model optimized based on a differential evolutionary algorithm for the prediction of multiple types of energy consumption in tin smelting. Redundant feature variables are eliminated using the distance correlation coefficient method, multi-kernel learning is introduced to improve the multi-output support vector regression model, and a differential evolutionary algorithm is used to optimize the model hyperparameters. The validity and superiority of the model was verified using the energy consumption data of a non-ferrous metal producer in Southwest China. The experimental results show that the proposed model outperformed multi-output Gaussian process regression (MGPR) and a multi-layer perceptron neural network (MLPNN) in terms of measurement capability. Finally, this paper uses a grey correlation analysis model to discuss the influencing factors on the integrated energy consumption of the tin smelting process and gives corresponding energy-saving suggestions. [ABSTRACT FROM AUTHOR]

In recent years, the expansive utilization of data mining techniques has revolutionized various fields, including the analysis of dance videos. This study leverages data mining to meticulously capture and analyze dance movements, thereby facilitating the enhancement and correction of dancers’ techniques. Within the scope of this research, images of dance movements extracted from videos are subjected to preprocessing, which involves grayscaling and thresholding, to prepare them for further analysis. Building on these processed images, this paper introduces a novel multi-feature dance action recognition approach. This method integrates several distinct features—directional gradient histogram features, optical flow directional histogram features, and audio features—employing a linear weighting scheme within a multi-core learning framework. The efficacy of the proposed approach is demonstrated through its performance on the FolkDance dance dataset, where it achieves a 3.5% increase in fusion accuracy over the traditional Dance Style Identification (DSI) method. Additionally, when compared with the Multi-Feature Learning-Combined (MFL-C) method, our approach shows an improvement of 0.6% in fusion accuracy. This research establishes a viable method for the recognition and classification of dance movements, laying a robust foundation for further inquiry and practical applications in this domain.

Searching for quantitative scientific prediction models and methods under uncertain hybrid information environment is helpful to the scientific decision-making of practical management problems. In this paper, we discuss the uncertain decision making problem with hybrid attributes and fuzzy decision objects. Considering the characteristic of hybrid attribute information, we introduce kernel functions to abstract the similarities from different types of attributes. On this basis, the kernel-based upper and lower approximations of arbitrary fuzzy decision objects over hybrid attribute information system are given under a multi-granularity framework, i.e., kernelized multi-granulation fuzzy rough sets, called KMGFRS in this paper. Then, the attribute reduction on hybrid attribute decision system based on KMGFRS is discussed. In addition, we combine KMGFRS and fuzzy k-nearest neighbors (FkNN) to propose a new prediction method without prior information and apply it to stroke risk assessment and prediction in clinical decision-making. Finally, we use real clinical data and UCI datasets for experiment analysis, and the results verify the applicability and validity of the model. [ABSTRACT FROM AUTHOR]

The real estate industry relies heavily on accurately predicting the price of a house based on numerous factors such as size, location, amenities, and season. In this study, we explore the use of machine learning techniques for predicting house prices by considering both visual cues and estate attributes. We collected a dataset (REPD-3000) of 3000 houses across 74 cities in the USA and annotated 14 estate attributes and five visual images for each house's exterior, interior-living room, kitchen, bedroom, and bathroom. We extracted features from the input images using convolutional neural network (CNN) and fed them along with the estate attributes into a multi-kernel deep learning regression model to predict the house price. Our model outperformed baseline models in extensive experiments, achieving the best result with a mean absolute error (MAE) of 16.60. We compared our model with a multi-kernel support vector regression and analyzed the impact of incorporating individual feature sets. In future, we plan to address class imbalance by having the same number of houses in each class and explore feature engineering for improving the model's performance. [ABSTRACT FROM AUTHOR]

This study aimed to investigate whether there are structural differences in the brains of professional artists who received formal training in the visual arts and non-artists who did not have any formal training or professional experience in the visual arts, and whether these differences can be used to accurately classify individuals as being an artist or not. Previous research using functional MRI has suggested that general creativity involves a balance between the default mode network and the executive control network. However, it is not known whether there are structural differences between the brains of artists and non-artists. In this study, a machine learning method called Multi-Kernel Learning (MKL) was applied to gray matter images of 12 artists and 12 non-artists matched for age and gender. The results showed that the predictive model was able to correctly classify artists from non-artists with an accuracy of 79.17% (AUC 88%), and had the ability to predict new cases with an accuracy of 81.82%. The brain regions most important for this classification were the Heschl area, amygdala, cingulate, thalamus, and parts of the parietal and occipital lobes as well as the temporal pole. These regions may be related to the enhanced emotional and visuospatial abilities that professional artists possess compared to non-artists. Additionally, the reliability of this circuit was assessed using two different classifiers, which confirmed the findings. There was also a trend towards significance between the circuit and a measure of vividness of imagery, further supporting the idea that these brain regions may be related to the imagery abilities involved in the artistic process. [ABSTRACT FROM AUTHOR]

Studies have found that human microbiome is associated with and predictive of human health and diseases. Many statistical methods developed for microbiome data focus on different distance metrics that can capture various information in microbiomes. Prediction models were also developed for microbiome data, including deep learning methods with convolutional neural networks that consider both taxa abundance profiles and taxonomic relationships among microbial taxa from a phylogenetic tree. Studies have also suggested that a health outcome could associate with multiple forms of microbiome profiles. In addition to the abundance of some taxa that are associated with a health outcome, the presence/absence of some taxa is also associated with and predictive of the same health outcome. Moreover, associated taxa may be close to each other on a phylogenetic tree or spread apart on a phylogenetic tree. No prediction models currently exist that use multiple forms of microbiome-outcome associations. To address this, we propose a multi-kernel machine regression (MKMR) method that is able to capture various types of microbiome signals when doing predictions. MKMR utilizes multiple forms of microbiome signals through multiple kernels being transformed from multiple distance metrics for microbiomes and learn an optimal conic combination of these kernels, with kernel weights helping us understand contributions of individual microbiome signal types. Simulation studies suggest a much-improved prediction performance over competing methods with mixture of microbiome signals. Real data applicants to predict multiple health outcomes using throat and gut microbiome data also suggest a better prediction of MKMR than that of competing methods. [ABSTRACT FROM AUTHOR]

Kernel-based methods exhibit well-documented performance in various nonlinearlearning tasks. Most of them rely on a preselected kernel, whose prudent choicepresumes task-specific prior information. Especially when the latter is notavailable, multi-kernel learning has gained popularity thanks to itsflexibility in choosing kernels from a prescribed kernel dictionary. Leveragingthe random feature approximation and its recent orthogonality-promotingvariant, the present contribution develops a scalable multi-kernel learningscheme (termed Raker) to obtain the sought nonlinear learning function `on thefly,' first for static environments. To further boost performance in dynamicenvironments, an adaptive multi-kernel learning scheme (termed AdaRaker) isdeveloped. AdaRaker accounts not only for data-driven learning of kernelcombination, but also for the unknown dynamics. Performance is analyzed interms of both static and dynamic regrets. AdaRaker is uniquely capable oftracking nonlinear learning functions in environments with unknown dynamics,and with with analytic performance guarantees. Tests with synthetic and realdatasets are carried out to showcase the effectiveness of the novel algorithms.

Multi-kernel learning (MKL) has been widely used in learning problems involving function learning tasks. Compared with single kernel learning approach which relies on a pre-selected kernel, the advantage of MKL is its flexibility results from combining a dictionary of kernels. However, inclusion of irrelevant kernels in the dictionary may deteriorate the accuracy of MKL, and increase the computational complexity. Faced with this challenge, a novel graph-aided framework is developed to select a subset of kernels from the dictionary with the assistance of a graph. Different graph construction and refinement schemes are developed based on incurred losses or kernel similarities to assist the adaptive selection process. Moreover, to cope with the scenario where data may be collected in a sequential fashion, or cannot be stored in batch due to the massive scale, random feature approximation are adopted to enable online function learning. It is proved that our proposed algorithms enjoy sub-linear regret bounds. Experiments on a number of real datasets showcase the advantages of our novel graph-aided algorithms compared to state-of-the-art alternatives. 1 [ABSTRACT FROM AUTHOR]

This paper proposes a new multi-kernel learning ensemble algorithm, called Ada- $L_{1}$ MKL-WSVR, which can be regarded as an extension of multi-kernel learning (MKL) and weighted support vector regression (WSVR). The first novelty is to add the $L_{1}$ norm of the weights of the combined kernel function to the objective function of WSVR, which is used to adaptively select the optimal base models and their parameters. In addition, an accelerated method based on fast iterative shrinkage thresholding algorithm (FISTA) is developed to solve the weights of the combined kernel function. The second novelty is to propose an integrated learning framework based on AdaBoost, named Ada- $L_{1}$ MKL-WSVR. In this framework, we integrate FISTA into AdaBoost. At each iteration, we optimize the weights of the combined kernel function and update the weights of the training samples at the same time. Then an ensemble regression function of a set of regression functions is output. Finally, two groups of the experiments are designed to verify the performance of our algorithm. On the first group of the experiments including eight datasets from UCI machine learning repository, the MAEs and RMSEs of Ada- $L_{1}$ MKL-WSVR are reduced by 11.14% and 9.08% on average, respectively. Furthermore, on the second group of the experiments including the COVID-19 epidemic datasets from eight countries, the MAEs and RMSEs of Ada- $L_{1}$ MKL-WSVR are reduced by 31.19% and 29.98% on average, respectively.

Multi-kernel learning methods are essential kernel learning methods. Still, the base kernel functions in most multi-kernel learning methods only with select kernel functions with shallow structures, which are weak for large-scale uneven data. We propose two types of acceleration models from a multidimensional perspective of the data: the neural tangent kernel (NTK)-based multi-kernel learning method is proposed, where the NTK kernel regressor is shown to be equivalent to an infinitely wide neural network predictor, and the NTK with deep structure is used as the base kernel function to enhance the learning ability of multi-kernel models; and a parallel computing kernel model based on data partitioning techniques. An RBF, POLY-based multi-kernel model is also proposed. All models use historical memory-based PSO (HMPSO) for efficient search of parameters within the model. Since NTK has a multi-layer structure and thus has a significant computational complexity, the use of a Monotone Disjunctive Kernel (MDK) to store and train Boolean features in binary achieves a 15–60% training time compression of NTK models in different datasets while obtaining a 1–25% accuracy improvement. [ABSTRACT FROM AUTHOR]

Antenna communication uses the sophisticated algorithms for Direction of Arrival estimation in the non uniform -linear array topology. Radar applications uses the mathematical models for Direction of Arrival estimation of the approaching signals. Recent developments in Basis pursuit solver algorithm have led to better beam forming techniques for Direction of Arrival algorithms. Sparse representation of signal helps in better signal analysis. This paper examines how to compute the direction of arrival of non-uniform linear array using sparse computation. A comparison between traditional techniques and sparse representation to estimate the direction of arrival is also studied. A novel method is proposed based on basis pursuit denoising (BPDN) to estimate the Direction of arrival. Simulation results are verified with the formulation developed for direction of arrival. A advanced manifold matrix is developed using the cumulative basis vector as the building element of the manifold matrix. MATLAB based simulation is developed with the advanced basis vector based manifold matrix to get the Direction of Arrival from multiple sources. [ABSTRACT FROM AUTHOR]

Identifying the key risk factors of disease from a large amount of clinical data is a prerequisite for further scientific decision-making. In medical practice, the clinical symptom information of patients usually includes various types of data. Meanwhile, the occurrence and development of diseases are joint result of the mutual influence factors. Therefore, there is usually a correlation between attributes. In this paper, we discuss a kind of hybrid attribute feature selection problem considering the correlation between attributes. Firstly, we take the identification of disease pathogenic factors in medical decision as the background, and construct a hybrid attribute decision system. Secondly, by introducing kernel alignment, the uncertain relationship between attributes is defined. Based on this, a three-way clustering model in attribute space is established. Furthermore, a feature selection method for hybrid attribute data based on three-way clustering in attribute space is proposed. Finally, we applied the proposed model to identify the pathogenic factors of stroke and used 279 clinical random samples for simulation analysis. The results verified the applicability and validity of the model. The main contributions of this paper include two aspects. In terms of theory, by introducing kernel alignment, a three-way clustering algorithm in attribute space is established. Meanwhile, a hybrid attribute feature selection method based on three-way clustering is proposed. In terms of application, the proposed method is applied to identify risk factors of stroke. [ABSTRACT FROM AUTHOR]

In the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals from the same joint remains challenging due to their similar brain spatial distribution. We designed experiments involving three motor imagery tasks—wrist extension, wrist flexion, and wrist abduction—with six participants. Based on this, a single-joint multi-task motor imagery EEG signal recognition method using Empirical Wavelet Decomposition and Multi-Kernel Extreme Learning Machine is proposed. This method employs Empirical Wavelet Decomposition (EWT) for modal decomposition, screening, and reconstruction of raw EEG signals, feature extraction using Common Spatial Patterns (CSP), and classification using Multi-Kernel Extreme Learning Machine (MKELM). After EWT processing, differences in time and frequency characteristics between EEG signals of different classes were enhanced, with the MKELM model achieving an average recognition accuracy of 91.93 %. We compared EWT with Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Local Mean Decomposition (LMD), and Wavelet Packet Decomposition (WPD). The results showed that the differences between various types of EEG signals processed by EWT were the most pronounced. The MKELM model outperformed traditional machine learning models such as Extreme Learning Machine (ELM), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) in terms of recognition performance, and also exhibited faster training speeds than deep learning models such as Bayesian Convolutional Neural Network (BCNN) and Attention-based Dual-scale Fusion Convolutional Neural Network (ADFCNN). In summary, the proposed method provides a new approach for achieving finer Brain-Computer Interface commands. • BCI technology relies on control commands for the same motor joint. • Empirical wavelet decomposition reveals detailed EEG time-frequency features. • Multi-kernel learning strategy enhances ELM nonlinearity. • Aquila optimization algorithm boosts model training efficiency. [ABSTRACT FROM AUTHOR]

Resting-state functional connectivity hypernetworks, in which multiple nodes can be connected, are an effective technique for diagnosing brain disease and performing classification research. Conventional functional hypernetworks can characterize the complex interactions within the human brain in a static form. However, an increasing body of evidence demonstrates that even in a resting state, neural activity in the brain still exhibits transient and subtle dynamics. These dynamic changes are essential for understanding the basic characteristics underlying brain organization and may correlate significantly with the pathological mechanisms of brain diseases. Therefore, considering the dynamic changes of functional connections in the resting state, we proposed methodology to construct resting state high-order functional hyper-networks (rs-HOFHNs) for patients with depression and normal subjects. Meanwhile, we also introduce a novel property (the shortest path) to extract local features with traditional local properties (cluster coefficients). A subgraph feature-based method was introduced to characterize information relating to global topology. Two features, local features and subgraph features that showed significant differences after feature selection were subjected to multi-kernel learning for feature fusion and classification. Compared with conventional hyper network models, the high-order hyper network obtained the best classification performance, 92.18%, which indicated that better classification performance can be achieved if we needed to consider multivariate interactions and the time-varying characteristics of neural interaction simultaneously when constructing a network. [ABSTRACT FROM AUTHOR]

Location proteomics seeks to provide automated high-resolution descriptions of protein location patterns within cells. Many efforts have been undertaken in location proteomics over the past decades, thereby producing plenty of automated predictors for protein subcellular localization. However, most of these predictors are trained solely from high-throughput microscopic images or protein amino acid sequences alone. Unifying heterogeneous protein data sources has yet to be exploited. In this paper, we present a pipeline called sequence, image, network-based protein subcellular locator (SIN-Locator) that constructs a multi-view description of proteins by integrating multiple data types including images of protein expression in cells or tissues, amino acid sequences and protein–protein interaction networks, to classify the patterns of protein subcellular locations. Proteins were encoded by both handcrafted features and deep learning features, and multiple combining methods were implemented. Our experimental results indicated that optimal integrations can considerately enhance the classification accuracy, and the utility of SIN-Locator has been demonstrated through applying to new released proteins in the human protein atlas. Furthermore, we also investigate the contribution of different data sources and influence of partial absence of data. This work is anticipated to provide clues for reconciliation and combination of multi-source data for protein location analysis. [ABSTRACT FROM AUTHOR]

Due to the need of practical application, multiple sensors are often used for data acquisition, so as to realize the multimodal description of the same object. How to effectively fuse multimodal data has become a challenge problem in different scenarios including remote sensing. Nonsparse multi-Kernel learning has won many successful applications in multimodal data fusion due to the full utilization of multiple Kernels. Most existing models assume that the nonsparse combination of multiple Kernels is infinitely close to a strict binary label matrix during the training process. However, this assumption is very strict so that label fitting has very little freedom. To address this issue, in this article, we develop a novel nonsparse multi-Kernel model for multimodal data fusion. To be specific, we introduce a label softening strategy to soften the binary label matrix which provides more freedom for label fitting. Additionally, we introduce a regularized term based on manifold learning to anti over fitting problems caused by label softening. Experimental results on one synthetic dataset, several UCI multimodal datasets and one multimodal remoting sensor dataset demonstrate the promising performance of the proposed model.

Resting-state functional connectivity hypernetworks, in which multiple nodes can be connected, are an effective technique for diagnosing brain disease and performing classification research. Conventional functional hypernetworks can characterize the complex interactions within the human brain in a static form. However, an increasing body of evidence demonstrates that even in a resting state, neural activity in the brain still exhibits transient and subtle dynamics. These dynamic changes are essential for understanding the basic characteristics underlying brain organization and may correlate significantly with the pathological mechanisms of brain diseases. Therefore, considering the dynamic changes of functional connections in the resting state, we proposed methodology to construct resting state high-order functional hyper-networks (rs-HOFHNs) for patients with depression and normal subjects. Meanwhile, we also introduce a novel property (the shortest path) to extract local features with traditional local properties (cluster coefficients). A subgraph feature-based method was introduced to characterize information relating to global topology. Two features, local features and subgraph features that showed significant differences after feature selection were subjected to multi-kernel learning for feature fusion and classification. Compared with conventional hyper network models, the high-order hyper network obtained the best classification performance, 92.18%, which indicated that better classification performance can be achieved if we needed to consider multivariate interactions and the time-varying characteristics of neural interaction simultaneously when constructing a network.

Using multi-kernel learning to deal with the non-linear relationship of data has become a new research topic in the field of multi-view subspace clustering. However, the existing methods have the following three defects: 1) the simple consensus kernel weighting strategy cannot give full play to the advantages of multiple kernels; 2) they are sensitive to non-Gaussian noise and their learning affinity matrices cannot meet the block diagonal properties required by clustering, resulting in low clustering performance; 3) the complementary feature information between the data of each view cannot be fully mined. In this paper, a novel robust multi-view subspace clustering method is proposed based on weighted multi-kernel learning and co-regularization (WMKMSC). Based on the self-expression learning framework, block diagonal regularizer (BDR), multi-kernel learning strategy and co-regularization are integrated into the proposed model. Especially, as a robust learning method, the mixture correntropy is used to construct a robust multi-kernel weighting strategy, which is helpful to learn the best consensus kernel. Our method is more effective and robust than several of the most advanced methods on five commonly used datasets.

Hyperspectral unmixing (HU) is regarded as an indispensable preprocessing procedure for many field of spectral data analysis because of the existence of mixed pixels. However, the unmixing algorithms are implemented under the presupposition of special mixing models. In other words, any unmixing algorithm only works on a special mixing model of the spectra. This leads to low generalization performance of most unmixing algorithms. To mitigate this problem, a robust unmixing method is proposed, which exploits dual views with adaptive weights for HU (AwDvHU). The proposed method utilizes multi-kernel learning to construct a high-dimensional space that can reflect the nonlinear interaction between spectra optimally. Then, through fusing the unmixing object of original data and the mapped high-dimensional features, the AwDvHU method takes full advantage of the complementary characteristics of features in dual views. Moreover, the AwDvHU method automatically learns the weights for dual views according to the importance of different feature spaces. Its effectiveness in unmixing is verified by experimental results both on the synthetic and real data. [ABSTRACT FROM AUTHOR]

The quality of surface waters plays a key role in the sustainability of ecological systems. Measuring water quality parameters (WQPs) is of high importance in the management of surface water resources. In this paper, contemporary-developed regression analysis was proposed to estimate the hard-to-measure parameters from those that can be measured easily. To this end, we proposed a novel modification of support vector regression (SVR), known as multiple-kernel support vector regression (MKSVR) algorithm. The MKSVR learns an optimal data representation for regression analysis by either linear or nonlinear combination of some precomputed kernels. For solving the optimization problem of the MKSVR, the particle swarm optimization (PSO) algorithm was used. The proposed algorithm was assessed using WQPs taken from Karun River, Iran. MKSVR was used to estimate chemical oxygen demand (COD) and biochemical oxygen demand (BOD) using nine WQPs as the input variables, namely electrical conductivity, sodium, calcium, magnesium, phosphate, nitrite, nitrate nitrogen, turbidity, and pH. The results of the proposed MKSVR were compared with those obtained using the SVR and Random Forest regression (RFR). The results showed that the MKSVR algorithm (correlation coefficient [R] = 0.8 and root mean squared error [RMSE] = 4.76 mg/l) increased the accuracy level of BOD prediction when compared with SVR (R = 0.68 and RMSE = 5.15 mg/l) and RFR (R = 0.77 and RMSE = 5.15 mg/l). In the case of COD estimation, the performance of a developed support vector machine (SVM) technique was satisfying. Overall, the use of MKSVR along with the PSO algorithm could demonstrate the superiority of the newly developed SVM technique for the WQPs estimation in the natural streams. [ABSTRACT FROM AUTHOR]

Multi-kernel learning methods are essential kernel learning methods. Still, the base kernel functions in most multi-kernel learning methods only with select kernel functions with shallow structures, which are weak for large-scale uneven data. We propose two types of acceleration models from a multidimensional perspective of the data: the neural tangent kernel (NTK)-based multi-kernel learning method is proposed, where the NTK kernel regressor is shown to be equivalent to an infinitely wide neural network predictor, and the NTK with deep structure is used as the base kernel function to enhance the learning ability of multi-kernel models; and a parallel computing kernel model based on data partitioning techniques. An RBF, POLY-based multi-kernel model is also proposed. All models use historical memory-based PSO (HMPSO) for efficient search of parameters within the model. Since NTK has a multi-layer structure and thus has a significant computational complexity, the use of a Monotone Disjunctive Kernel (MDK) to store and train Boolean features in binary achieves a 15–60% training time compression of NTK models in different datasets while obtaining a 1–25% accuracy improvement.

Blasting operations are widely and frequently used for rock excavation in Civil and Mining constructions. Flyrock is one of the most important issues induced by blasting operations in open pit mines, and therefore needs to be well predicted in order to identify the safety zone to prevent the potential injuries. For this purpose, 234 sets of blasting data were collected from Sungun Copper Mine site, and a stacked deep multi-kernel learning (SD-MKL) framework was proposed to estimate the blast induced flyrock with confidence accuracy. The proposed model uses the stacking-based representation learning framework (S-RL) to achieve deep learning on small-scale training sets. A multi-kernel learning model (MKL) is used as the base module of S-RL framework, which uses a multi-feature fusion strategy to generate multiple kernels with different kernel length in order to reduce the effort in tuning hyperparameters. In addition, this study further enhanced the predictive capability of SD-MKL by introducing the boosting method into the S-RL framework and hence proposed a boosted SD-MKL model. For comparison purpose, several existing machine learning models were implemented, i.e., kernel ridge regression (KRR), support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), ensemble deep random vector functional link (edRVFL), SD-KRR and SD-SVM. Our experimental results showed that the proposed boosted SD-MKL achieved the best overall performance, with the lowest RMSE of 0.21/1.73, MAE of 0.08/0.78, and the highest VAF of 99.98/99.24. • A stacked deep learning multi-kernel learning model (SD-MKL) for flyrock prediction. • Use stacked-representation learning (S-RL) framework to achieve deep learning. • Use the multi-kernel learning model with multi-feature fusion strategy to learn the relationship between generated feature and original feature. • Use boosting method to improve the prediction accuracy of SD-MKL. [ABSTRACT FROM AUTHOR]

Existing methods for egocentric activity recognition are mostly based on extracting motion characteristics from videos. On the other hand, ubiquity of wearable sensors allow acquisition of information from different sources. Although the increase in sensor diversity brings out the need for adaptive fusion, most of the studies use pre-determined weights for each source. In addition, there are a limited number of studies making use of optical, audio and wearable sensors. In this work, we propose a new framework that adaptively weighs the visual, audio and sensor features in relation to their discriminative abilities. For that purpose, multi-kernel learning (MKL) is used to fuse multi-modal features where the feature and kernel selection/weighing and recognition tasks are performed concurrently. Audio-visual information is used in association with the data acquired from wearable sensors since they hold information on different aspects of activities and help building better models. The proposed framework can be used with different modalities to improve the recognition accuracy and easily be extended with additional sensors. The results show that using multi-modal features with MKL outperforms the existing methods. [ABSTRACT FROM AUTHOR]

Given the increasing amounts of data and high feature dimensionalities in forecasting problems, it is challenging to build regression models that are both computationally efficient and highly accurate. Moreover, regression models commonly suffer from low interpretability when using a single kernel function or a composite of multi-kernel functions to address nonlinear fitting problems. In this paper, we propose a bi-sparse optimization-based regression (BSOR) model and corresponding algorithm with reconstructed row and column kernel matrices in the framework of support vector regression (SVR). The BSOR model can predict continuous output values for given input points while using the zero-norm regularization method to achieve sparse instance and feature sets. Experiments were run on 16 datasets to compare BSOR to SVR, linear programming SVR (LPSVR), least squares SVR (LSSVR), multi-kernel learning SVR (MKLSVR), least absolute shrinkage and selection operator regression (LASSOR), and relevance vector regression (RVR). BSOR significantly outperformed the other six regression models in predictive accuracy, identification of the fewest representative instances, selection of the fewest important features, and interpretability of results, apart from its slightly high runtime. [ABSTRACT FROM AUTHOR]

• We propose an efficient method for diagnosing ASD. • Dynamic functional connectivity features have complementary information. • We proposed an improved multi-task feature selection method. • A multi-kernel SVM method is applied. • Our method achieves classification accuracy of 76.8%. Accurate diagnosis of autism spectrum disorder (ASD), which is a neurodevelopmental disorder and often accompanied by abnormal social skills, communication skills, interests and behavior patterns, has always been a challenging task in clinical practice. Recent studies have shown great potential for using fMRI data to distinguish ASD from typical control (TC). However, it has always been a challenging problem to extract which features from fMRI data and how to combine these different types of features to achieve improved ASD/TC classification performance. To address this problem, in this study we propose an improved ASD/TC classification framework based on dynamic functional connectivity (DFC) and multi-task feature selection. Our proposed ASD/TC classification framework is evaluated on 871 subjects with fMRI data from the Autism Brain Imaging Data Exchange I (ABIDE I) via a 10-fold cross validation strategy. Experimental results show that our proposed method achieves an accuracy of 76.8% and an area under the receiver operating characteristic curve (AUC) of 0.81 for ASD/TC classification. In addition, compared with some existing state-of-the-art methods, our proposed method achieves better accuracy and AUC for ASD/TC classification. Overall, our proposed ASD/TC classification framework is effective and promising for automatic diagnosis of ASD in clinical practice. [ABSTRACT FROM AUTHOR]

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets. [ABSTRACT FROM AUTHOR]

Despite excellent performance shown by spatially regularized discriminative correlation filters (SRDCF) for visual tracking, some issues remain open that hinder further boosting their performance: first, SRDCF utilizes multiple training images to formulate its model, which makes it unable to exploit the circulant structure of the training samples in learning, leading to high computational burden; second, SRDCF is unable to efficiently exploit the powerfully discriminative nonlinear kernels, further negatively affecting its performance. In this paper, we present a novel spatial-temporally regularized complementary kernelized CFs (STRCKCF) based tracking approach. First, by introducing spatial-temporal regularization to the filter learning, the STRCKCF formulates its model with only one training image, which can not only facilitate exploiting the circulant structure in learning, but also reasonably approximate the SRDCF with multiple training images. Furthermore, by incorporating two types of kernels whose matrices are circulant, the STRCKCF is able to fully take advantage of the complementary traits of the color and HOG features to learn a robust target representation efficiently. Besides, our STRCKCF can be efficiently optimized via the alternating direction method of multipliers (ADMM). Extensive evaluations on OTB100 and VOT2016 visual tracking benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the-art trackers with a speed of 40 fps on a single CPU. Compared with SRDCF, STRCKCF provides a 8 × speedup and achieves a gain of 5.5% AUC score on OTB100 and 8.4% EAO score on VOT2016. [ABSTRACT FROM AUTHOR]

Advances in digital electronics, wireless communications, and electro-mechanical systems technology have revolutionized the society and economy across the globe by enabling the development of low-cost, low-power, and multi-functional sensor nodes, from which the sensor networks are realized by leveraging the features of sensing, data processing, and communication present in these nodes. Though the energy of the wireless sensor network (WSN) nodes is limited, the detection of existing intrusion detection systems in WSN is weakly accurate further. To reduce the energy consumption of nodes in WSNs during detection processing, we propose a hierarchical intrusion detection model that clusters the nodes in a WSN according to their functions. Even more, to improve the detection accuracy of abnormal behavior of the WSN intrusion detection system and reduce the false alarm rate, it is considered in this research the usage of the classification algorithm of kernel extreme learning machine, following to Mercer Property to synthesize multi-kernel functions. We realize the optimal linear combination by testing and applying the multi-kernel function and build a multi-kernel extreme learning machine to WSN intrusion detection systems. Simulation results show that the system not only guarantees a high detection accuracy but also dramatically reduces the detection time, being well suited for resource-constrained WSNs. [ABSTRACT FROM AUTHOR]

The classification of mild cognitive impairment (MCI), which is a early stage of Alzheimer's disease and is associated with brain structural and functional changes, is still a challenging task. Recent studies have shown great promise for improving the performance of MCI classification by combining multiple structural and functional features, such as grey matter volume and clustering coefficient. However, extracting which features and how to combine multiple features to improve the performance of MCI classification have always been challenging problems. To address these problems, in this study we propose a new method to enhance the feature representation of multi-modal MRI data by combining multi-view information to improve the performance of MCI classification. Firstly, we extract two structural features (including grey matter volume and cortical thickness) and two functional features (including clustering coefficient and shortest path length) of each cortical brain region based on automated anatomical labeling (AAL) atlas from both T1w MRI and rs-fMRI data of each subject. Then, in order to obtain features that are more helpful in distinguishing MCI subjects, an improved multi-task feature selection method, namely MTFS-gLASSO-TTR, is proposed. Finally, a multi-kernel learning algorithm is adopted to combine multiple features to perform the MCI classification task. Our proposed MCI classification method is evaluated on 315 subjects (including 105 LMCI subjects, 105 EMCI subjects and 105 NCs) with both T1w MRI and rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed method achieves an accuracy of 88.5% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.897 for LMCI/NC classification, an accuracy of 82.7% and an AUC of 0.832 for EMCI/NC classification, and an accuracy of 79.6% and an AUC of 0.803 for LMCI/EMCI classification, respectively. In addition, by comparison, the accuracy and AUC values of our proposed method are better than those of some existing state-of-the-art methods in MCI classification. Overall, our proposed MCI classification method is effective and promising for automatic diagnosis of MCI in clinical practice. [ABSTRACT FROM AUTHOR]

Lithofacies identification is a crucial work in reservoir characterization and modeling. The vast inter-well area can be supplemented by facies identification of seismic data. However, the relationship between lithofacies and seismic information that is affected by many factors is complicated. Machine learning has received extensive attention in recent years, among which support vector machine (SVM) is a potential method for lithofacies classification. Lithofacies classification involves identifying various types of lithofacies and is generally a nonlinear problem, which needs to be solved by means of the kernel function. Multi-kernel learning SVM is one of the main tools for solving the nonlinear problem about multi-classification. However, it is very difficult to determine the kernel function and the parameters, which is restricted by human factors. Besides, its computational efficiency is low. A lithofacies classification method based on local deep multi-kernel learning support vector machine (LDMKL-SVM) that can consider low-dimensional global features and high-dimensional local features is developed. The method can automatically learn parameters of kernel function and SVM to build a relationship between lithofacies and seismic elastic information. The calculation speed will be expedited at no cost with respect to discriminant accuracy for multi-class lithofacies identification. Both the model data test results and the field data application results certify advantages of the method. This contribution offers an effective method for lithofacies recognition and reservoir prediction by using SVM. [ABSTRACT FROM AUTHOR]

Smart inverters have been advocated as a fast-responding mechanism for voltage regulation in distribution grids. Nevertheless, optimal inverter coordination can be computationally demanding, and preset local control rules are known to be subpar. Leveraging tools from machine learning, the design of customized inverter control rules is posed here as a multi-task learning problem. Each inverter control rule is modeled as a possibly nonlinear function of local and/or remote control inputs. Given the electric coupling, the function outputs interact to yield the feeder voltage profile. Using an approximate grid model, inverter rules are designed jointly to minimize a voltage deviation objective based on anticipated load and solar generation scenarios. Each control rule is described by a set of coefficients, one for each training scenario. To reduce the communication overhead between the grid operator and the inverters, we devise a voltage regulation objective that is shown to promote parsimonious descriptions for inverter control rules. Numerical tests using real-world data on a benchmark feeder demonstrate the advantages of the novel nonlinear rules and explore the trade-off between voltage regulation and sparsity in rule descriptions. [ABSTRACT FROM AUTHOR]

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Omics fusion has emerged as a crucial preprocessing approach in medical image processing, significantly assisting several studies. One of the challenges encountered in integrating omics data is the unpredictability arising from disparities in data sources and medical imaging equipment. Due to these differences, the distribution of omics futures exhibits spatial heterogeneity, diminishing their capacity to enhance subsequent tasks. To overcome this challenge and facilitate the integration of their joint application to specific medical objectives, this study aims to develop a fusion methodology for nasopharyngeal carcinoma (NPC) distant metastasis prediction to mitigate the disparities inherent in omics data. The multi-kernel late-fusion method can reduce the impact of these differences by mapping the features using the most suiTable single-kernel function and then combining them in a high-dimensional space that can effectively represent the data. The proposed approach in this study employs a distinctive framework incorporating a label-softening technique alongside a multi-kernel-based Radial basis function (RBF) neural network to address these limitations. An efficient representation of the data may be achieved by utilizing the multi-kernel to map the inherent features and then merging them in a space with many dimensions. However, the inflexibility of label fitting poses a constraint on using multi-kernel late-fusion methods in complex NPC datasets, hence affecting the efficacy of general classifiers in dealing with high-dimensional characteristics. The label softening increases the disparity between the two cohorts, providing a more flexible structure for allocating labels. The proposed model is evaluated on multi-omics datasets, and the results demonstrate its strength and effectiveness in predicting distant metastasis of NPC patients., Competing Interests: Declaration of competing interest The authors reported no potential conflict of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

As known to us, tremendous efforts have been made to exploit information from borrower's credit for loan evaluation in P2P lending, but seldom researches have explored information from investors and borrowers. To this end, we propose an integrated loan evaluation model that exploits and fuses multi-source information from both the borrower and the investor for improving investment decisions in P2P lending. First, based on the borrower's credit, we build a kernel-based credit risk model to quantitatively evaluate each loan. Second, we build an investor composition model that exploits information from the investor's investment behavior for loan evaluation. Then, based on the above two quantitative models and correlation, we define a multi-kernel weight and develop an integrated loan assessment model that can evaluate a loan with both the return and risk. Furthermore, based on the integrated information loan evaluation model, we formalize the investment decisions in P2P lending as a portfolio optimization problem with boundary constraints to help investor make better investment decisions. To validate the proposed model, we perform extensive experiments on the realworld data from the world's largest P2P lending marketplace. Experimental results reveal that the integrated loan evaluation model can significantly enhance investment performance beyond the existing model in P2P market and other baseline models. [ABSTRACT FROM AUTHOR]

Aim at the low real-time performance and high false alarm rate of the traditional fall detection model, AdaBoost multi-core support vector machine model (ADB-MKSVM) was proposed which was used to detect and identify the falling action. Based on the improved AdaBoost model framework, the model took multi-core support vector machine as the basis classifier and assembled these basis classifiers to form a stronger final classifier. According to the distribution of human movement data and whether the classification of each sample in each training set is correct or not, and the overall classification accuracy last time, the weight of each sample was determined. The dynamic weight allocation method was used to improve the recognition rate of the fall action. The test results showed that this model had good classification performance, and the method of binding the sensor on the waist position could effectively improve the detection effect of the fall action. The accuracy rate was 99. 33%,the fall detection rate was 63. 6%,and the false detection rate was 1. 62% . [ABSTRACT FROM AUTHOR]

In this paper, co-regularized kernel ensemble regression scheme is brought forward. In the scheme, multiple kernel regressors are absorbed into a unified ensemble regression framework simultaneously and co-regularized by minimizing total loss of ensembles in Reproducing Kernel Hilbert Space. In this way, one kernel regressor with more accurate fitting precession on data can automatically obtain bigger weight, which leads to a better overall ensemble performance. Compared with several single and ensemble regression methods such as Gradient Boosting, Tree Regression, Support Vector Regression, Ridge Regression and Random Forest, our proposed method can achieve best performances of regression and classification tasks on several UCI datasets. [ABSTRACT FROM AUTHOR]

Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. Especially when the latter is not available, multi-kernel learning has gained popularity thanks to its exibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation and its recent orthogonality-promoting variant, the present contribution develops a scalable multi-kernel learning scheme (termed Raker) to obtain the sought nonlinear learning function 'on the y,' first for static environments. To further boost performance in dynamic environments, an adaptive multi-kernel learning scheme (termed AdaRaker) is developed. AdaRaker accounts not only for data-driven learning of kernel combination, but also for the unknown dynamics. Performance is analyzed in terms of both static and dynamic regrets. AdaRaker is uniquely capable of tracking nonlinear learning functions in environments with unknown dynamics, and with with analytic performance guarantees. Tests with synthetic and real datasets are carried out to showcase the effectiveness of the novel algorithms. [ABSTRACT FROM AUTHOR]

The fractional amplitude of low-frequency fluctuations (fALFF) has been widely used as potential clinical biomarkers for resting-state functional-magnetic-resonance-imaging-based schizophrenia diagnosis. How-ever, previous studies usually measure the fALFF with specific bands from 0.01 to 0.08 Hz, which cannot fully delineate the complex variations of spontaneous fluctuations in the resting-state brain. In addition, fALFF data are intrinsically constrained by the brain structure, but most of the traditional methods have not consider it in feature selection. For addressing these problems, we propose a model to classify schizophrenia in multifrequency bands with tree-guided group sparse learning. In detail, we first acquire the fALFF data in multifrequency bands (i.e., slow-5: 0.01–0.027 Hz, slow-4: 0.027–0.073 Hz, slow-3: 0.073–0.198 Hz, and slow-2: 0.198–0.25 Hz). Then, we divide the whole brain into different candidate patches and select those significant patches related to schizophrenia using random forest-based important score. Moreover, we use tree-structured sparse learning method for feature selection with the above patch spatial constraint. Finally, considering biomarkers from multifrequency bands can reflect complementary information among multiple-frequency bands, we adopt the multikernel learning method to combine features of multifrequency bands for classification. Our experimental results show that these biomarkers from multifrequency bands can achieve a classification accuracy of 91.1% on 17 schizophrenia patients and 17 healthy controls, further demonstrating that the multifrequency bands analysis can better account for classification of schizophrenia. [ABSTRACT FROM AUTHOR]

Brain disorders, such as Autism Spectrum Disorder (ASD), alter brain functional (from fMRI) and structural (from diffusion MRI) connectivities at multiple levels and in varying degrees. While unraveling such alterations have been the focus of a large number of studies, morphological brain connectivity has been out of the research scope. In particular, shape-to-shape relationships across brain regions of interest (ROIs) were rarely investigated. As such, the use of networks based on morphological brain data in neurological disorder diagnosis, while leveraging the advent of machine learning, could complement our knowledge on brain wiring alterations in unprecedented ways. In this paper, we use conventional T1-weighted MRI to define morphological brain networks (MBNs), each quantifying shape relationship between different cortical regions for a specific cortical attribute at both low-order and high-order levels. While typical brain connectomes investigate the relationship between two ROIs, we propose high-order MBN which better captures brain complex interactions by modeling the morphological relationship between pairs of ROIs. For ASD identification, we present a connectomic manifold learning framework, which learns multiple kernels to estimate a similarity measure between ASD and normal controls (NC) connectional features, to perform dimensionality reduction for clustering ASD and NC subjects. We benchmark our ASD identification method against both supervised and unsupervised state-of-the-art methods, while depicting the most discriminative high- and low-order relationships between morphological regions in the left and right hemispheres.