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Spectral analysis and Information-theory are two powerful and successful frameworks for feature selection in supervised classification problems. However, most of the methods developed under these frameworks have been introduced for handling exclusively numerical or non- numerical data. In this paper, we propose a supervised filter feature selection method that combines Spectral Feature Selection and Information-theory based redundancy analysis for selecting relevant and non-redundant features in supervised mixed datasets; i.e., datasets where the objects are described simultaneously by both, numerical and non-numerical features. To demonstrate the effectiveness of our proposed supervised filter feature selection method, we conducted several experiments on 40 public real-world datasets. Additionally, we compare our method against relevant state-of-the-art supervised filter methods for numerical, non-numerical, and mixed data. From this comparison, our method, in general, obtains better results than the results obtained by the other evaluated filter feature selection methods.

This letter investigates a multiuser mobile-edge computing (MEC) system with data compression technique to reduce the redundancy of sensed data, save the energy consumption and reduce the latency for wireless transmission. In this regard, the problem of minimizing the total energy consumption by jointly optimizing the transmission bandwidth allocation and data compression ratio is firstly considered under the constraints of latency and limited computation resource. Moreover, Lagragian and iterative-based algorithms are proposed to solve the non-convex optimization problem. Finally, simulation results are shown to verify the efficiency of the proposed algorithms and demonstrate that the application of data compression technique into MEC system can effectively reduce the energy consumption and latency.

We apply the method of moments to prove a recent conjecture of Haikin, Zamir and Gavish concerning the distribution of the singular values of random subensembles of Paley equiangular tight frames. Our analysis applies more generally to real equiangular tight frames of redundancy 2, and we suspect similar ideas will eventually produce more general results for arbitrary choices of redundancy.

Contextual information can be used in recommender systems to make recommendation more efficient. Recent research has made progress in combining contextual information into representation models for recommendations. However, the existing approaches do not well address the problem of data sparsity, and they suffer from context redundancy. To deal with these problems, this paper proposes a context-aware recommendation approach based on embedded feature selection. It gets rid of context redundancy by generating a minimum subset of all contextual information and allocates the weight to each context appropriately. Experiments on the restaurant recommendation shows that the proposed approach has better performance.

Kernel principal component analysis (KPCA) is a well-established data-driven process modeling and monitoring framework that has long been praised for its performances. However, it is still not optimal for large-scale and uncertain systems. Applying KPCA usually takes a long time and a significant storage space when big data are utilized. In addition, it leads to a serious loss of information and ignores uncertainties in the processes. Consequently, in this paper, two uncertain nonlinear statistical fault detection methods using an interval reduced kernel principal component analysis (IRKPCA) are proposed. The main objective of the proposed methods is twofold. Firstly, reduce the number of observations in the data matrix through two techniques: a method, called IRKPCA $_{ED}$ , is based on Euclidean distance between samples as dissimilarity metric such that only one observation is kept in case of redundancy to build the reduced reference KPCA model, and another method, called IRKPCA $_{PCA}$ , is established on the PCA algorithm to treat the hybrid correlations between process variables and extract a reduced number of observations from the training data matrix. Secondly, address the problem of uncertainties in systems using a latent-driven technique based on interval-valued data. Taking into account sensors uncertainties via IRKPCA ensures better monitoring by reducing the computational and storage costs. The study demonstrated the feasibility and effectiveness of the proposed approaches for faults detection in two real world applications: Tennessee Eastman (TE) process and real air quality monitoring network (AIRLOR) data.

Vision is the main way for people to perceive and recognize the world. In this paper, four categories of the redundant information of video encoding, spatial redundancy, time redundancy, visual redundancy and statistical redundancy, are first introduced. Spatial redundancy is redundant information existing in static frames, time redundancy is the luminance and chrominance correlation between adjacent frames in the video sequence, visual redundancy is information that the human visual system cannot perceive, statistical redundancy is the expression redundancy of the information source symbols. Then the performance assessment of video coding are introduced. And the future development of video coding and conclusion are finally discussed in this paper.

To make full use of the key information in frame-level features, a DNN-based model for speech enhancement is proposed using self-attention on the feature dimension. Two improvement strategies are adopted to strengthen the attention of the fully connected layers to the effective information in the features. First, the model introduces the fusion of feature domains on the input features, using a 136-dimensional combination of features including the MFCC, AMS, RASTA-PLP, cochleagram, and PNCC. The fusion of features complements information from different domains, including the mel domain and gammatone domain, thus providing more effective information for self-attention. Second, a feature-level self-attention mechanism is applied to the output of the fully connected layer to obtain the information related to the task. The feature-level attention enables the fully connected layers to capture the internal correlations between different features and to reduce the redundancy brought by multiple features. The experimental results show that under the matched noisy condition, compared to the noisy signals, the proposed algorithm increased the PESQ, fwsegSNR and STOI by 40.92%, 60.2% and 8.31%, respectively, and by 23.64%, 32.55% and 3.4%, respectively, under the mismatched noisy condition. Comparisons between different neural networks indicate that the proposed algorithm is superior to the compared algorithms in both the matched and mismatched situations using fewer context frames. Therefore, the proposed model can effectively utilize the key information of the features to suppress the noise, thereby improving the speech quality and generalizing the mismatched samples.

Due to the low compression performance of traditional compression models, we have developed a new HOA based Fractional KCA with 2D-DWT for improving the multispectral image quality. In this paper, we present a novel multispectral image compression method for improving the complexity by maintaining quality reconstruction and also reducing the size of the storage of multispectral images. Initially, Karhunen–Loeve transform (KLT) is used to remove the spatial redundancies. In the second stage, 2D DWT is used to eliminate the intraband spatial redundancies. In the third stage, Fractional KCA (FKCA) is applied to improve the post-transformation process. FKCA is connected to the band of all wavelet sub-bands to minimize the spatial redundancy between intra sub-bands. Finally, the Hybrid Optimal algorithm (HOA) based FKCA is used to eliminate the residual and information redundancy among the neighboring bands. The experimental analysis of proposed 2D-DWT based Fractional KCA shows that the model improves the performance of compression data in terms of PSNR, MSSI, and VIF. Also, the multispectral image dataset shows the proposed compression model outperforms the existing compression models such as FKLT + PCA, ADWT + OADL, and DWT + DCT

The tight combination model improves the positioning accuracy of the Global Navigation Satellite System (GNSS) in complex environments by increasing the redundancy of observation. However, the ambiguity cannot be calculated directly because of the correlation between it and the phase difference inter-system bias (DISB) in the model. This paper proposes a method of DISB estimation based on the principle of maximum ratio. From the data analysis, for the standard deviation of code DISB, the improvement of the method can up to 0·179 m with the poor quality data. In addition, compared to the parameter combination method, the standard deviation of all the phase DISB was deceased with the method in the paper. About the phase DISB of GPS L1/Galileo E1, the standard deviation decreased from 0·014/0·022/0·009/0·051 cycles to 0·006/0·015/0·004/0·029 cycles of four baselines, which represents the improvement of 57·14/31·82/55·56/43·14%. About the phase DISB of GPS L1/BDS B1, the standard deviation decreased from 0·014/0·061/0·010/0·052 cycles to 0·002/0·005/0·009/0·004 cycles of four baselines, which represents the improvement of 85·71/91·80/10·00/92·31%.

In this paper, a novel compression framework based on 3D point cloud data is proposed for telepresence, which consists of two parts. One is implemented to remove the spatial redundancy, i.e., a robust Bayesian framework is designed to track the human motion and the 3D point cloud data of the human body is acquired by using the tracking 2D box. The other part is applied to remove the temporal redundancy of the 3D point cloud data. The temporal redundancy between point clouds is removed by using the motion vector, i.e., the most similar cluster in the previous frame is found for the cluster in the current frame by comparing the cluster feature and the cluster in the current frame is replaced by the motion vector for compressing the current frame. The first, the B-SHOT (binary signatures of histograms orientation) descriptor is applied to represent the point feature for matching the corresponding point between two frames. The second, the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current frame. The matching operation is exploited to find the corresponding clusters between the point cloud data of two frames. Finally, the cluster information in the current frame is replaced by the motion vector for compressing the current frame and the unsuccessfully matched clusters in the current and the motion vectors are transmitted into the remote end. In order to reduce calculation time of the B-SHOT descriptor, we introduce an octree structure into the B-SHOT descriptor. In particular, in order to improve the robustness of the matching operation, we design the cluster feature to estimate the similarity between two clusters. Experimental results have shown the better performance of the proposed method due to the lower calculation time and the higher compression ratio. The proposed method achieves the compression ratio of 8.42 and the delay time of 1228 ms compared with the compression ratio of 5.99 and the delay time of 2163 ms in the octree-based compression method under conditions of similar distortion rate.

Unsupervised feature selection is an indispensable pre-processing step in many data mining and pattern recognition tasks where the unlabeled high dimensional data are ubiquitous. Most of existing methods fail to explore the local geometric structure consistency (preservation) of the input data and minimize redundancy of selected features simultaneously. In this paper we propose a novel unsupervised feature selection method which jointly integrates the local geometric structure consistency and redundancy minimization (JLSPRM) into an unified framework. JLSPRM utilizes nonnegative spectral analysis to learn the cluster labels of the input data, then the local geometric structure consistency is developed to make the learned cluster labels more accurate, during which the feature selection operation is performed. To minimize the redundancy rate among selected features, the maximal information coefficient (MIC) is utilized to evaluate the correlation of the pairwise features. Besides, the l2,1-norm is exerted on feature selection matrix which makes the framework decent for selecting features. An efficient iterative optimization algorithm is designed to obtain the solution of the unsupervised feature selection model. The superiority and effectiveness of our proposed approach over the state-of-the-art feature selection methods have also been validated through the extensive experiments on nine benchmark datasets.

Unmanned aerial vehicles (UAVs)-based sensor network is an effective mechanism for recognising and tracking of manoeuvring targets as well as expanding the monitoring coverage in the battlefield. However, there exists redundancy among the spectrum data collected by a UAV monitor within a data collection period, which may waste storage space and reduce the speed of data uploaded to the control centre. The authors assume that each UAV is equipped with an edge computing server and propose a delta compression method, which can save cache space and transfer time. First, they present a cost model and evaluation model for delta compression of the COPY/ADD class. Then an optimisation problem is formulated aiming to obtain the optimal delta encoding. Additionally, a maximal total length of copied fragments (MTLC) algorithm is proposed to find more mutually separated L-grams common fragments between the data collected at two adjacent moments. Theoretical analysis proves that the MTLC algorithm can generate a good delta encoding with the maximum total length and then the minimum total number of the COPYs. Moreover, numerical results show that MTLC has better performance in constructing a good delta encoding than the simple greedy and hash suffix array delta algorithms.

The tight lower bound on the redundancy of optimal prefix-free codes in terms of a given symbol probability (not necessarily the largest or the smallest one) has been derived in the literature. The first goal of this paper is to derive the tight lower bounds in terms of $j$ ( $j>1$ ) known symbol probabilities of the source using some properties of Kullback-Leibler distance. Since fix-free codes are special prefix-free codes (for which no codeword is a suffix of the other codewords), it is clear that all lower bounds on the redundancy of optimal prefix-free codes are also valid for fix-free ones. Accordingly, the second question of the paper is on the tightness of the derived lower bounds for optimal fix-free codes. It is proven that these lower bounds are tight for optimal fix-free codes if $j\leq 3$ , and are not tight for $j\geq 4$ . Also, it is shown that the tight lower bound in terms of the probability of the most likely symbol is the same for optimal prefix-free and optimal fix-free codes.

It is a research hotspot to restore decoded videos with existing bitstreams by applying deep neural network to improve compression efficiency at decoder-end. Existing research has verified that the utilization of redundancy at decoder-end, which is underused by the encoder, can bring an increase of compression efficiency. However, most existing research neglects the abundant multi-scale information among video frames as a typical type of such redundancy. It remains an interesting yet challenging topic how to build an effective, interpretable and fast deep neural network for the purpose of using the multi-scale similarity at decoder-end and further enhancing compression efficiency. To this end, this paper considers the use of underused inter multi-scale information and proposes the Fast Multi-Scale Deep Decoder (Fast MSDD) for the state-of-the-art video coding standard HEVC. The advantages of Fast MSDD are three-fold. First, it achieves a higher coding efficiency without modifying any encoding algorithm. Second, Fast MSDD is interpretable based on the framework of using the underused redundancy. Third, it guarantees the model's inference speed while fully using the multi-scale similarity among video frames. Extensive experimental results verify Fast MSDD's effectiveness, interpretability, and computational efficiency. Fast MSDD obtains averagely 14.3%, 10.8%, 8.5% and 7.6% BD gains for AI, LP, LB and RA respectively. Compared with our previous work MSDD, Fast MSDD achieves increases of 59.3%, 49.1%, 61.0% and 29.3%. Meanwhile, 16.9%, 11.2%, 9.2% and 8.3% BD gains are observed on videos with scale changes, which validate the interpretability of the proposed method. Furthermore, Fast MSDD can save at most 56.3% time compared to MSDD.

Coding schemes are presented that provide the ability to locate computational errors above a prescribed threshold while using analog resistive devices for approximate real vector–matrix multiplication. In such devices, the matrix is programmed into the device by setting an array of resistors to have conductances proportional to the respective entries in the matrix. In the coding scheme that is considered in this work, redundancy columns are appended so that each row in the programmed matrix forms a codeword of a prescribed linear code ${\mathcal {C}}$ over the real field; the result of the multiplication of any input real row vector by the matrix is then also a codeword of ${\mathcal {C}}$ . While error values within $\pm \delta $ in the entries of the result are tolerable (for some prescribed $\delta > 0$ ), outlying errors, with values outside the range $\pm \Delta $ (for a prescribed $\Delta \ge \delta $ ) should be located and corrected. As a design and analysis tool for such a setting, a certain functional is defined for the code ${\mathcal {C}}$ , through which a characterization is obtained for the number of outlying errors that can be handled, as a function of the ratio $\Delta /\delta $ . Several code constructions are then presented, primarily for the case of single outlying error handling. For this case, the coding problem is shown to be related to certain extremal problems on convex polygons.

In this paper we study array-based codes over graphs for correcting multiple node failures. These codes have applications to neural networks, associative memories, and distributed storage systems. We assume that the information is stored on the edges of a complete undirected graph and a node failure is the event where all the edges in the neighborhood of a given node have been erased. A code over graphs is called $\rho $ -node-erasure-correcting if it allows to reconstruct the erased edges upon the failure of any $\rho $ nodes or less. We present a binary optimal construction for double-node-erasure correction together with an efficient decoding algorithm, when the number of nodes is a prime number. Furthermore, we extend this construction for triple-node-erasure-correcting codes when the number of nodes is a prime number and two is a primitive element in $\mathbb {Z}_{n}$ . These codes are at most a single bit away from optimality.

Feature selection is an important data preprocessing method before classification. Multi-objective optimization algorithms have been proved an effective way to solve feature selection problems. However, there are few studies on multi-objective optimization feature selection methods for multi-label data. In this paper, a multi-objective multi-label filter feature selection algorithm based on two particle swarms (MOMFS) is proposed. We use mutual information to measure the relevance between features and label sets, and the redundancy between features, which are taken as two objectives. In order to avoid Particle Swarm Optimization (PSO) from falling into the local optimum and obtaining a false Pareto front, we employ two swarms to optimize the two objectives separately and propose an improved hybrid topology based on particle’s fitness value. Furthermore, an archive maintenance strategy is introduced to maintain the distribution of archive. In order to study the effectiveness of the proposed algorithm, we select five multi-label evaluation criteria and perform experiments on seven multi-label data sets. MOMFS is compared with classic single-objective multi-label feature selection algorithms, multi-objective filter and wrapper feature selection algorithms. The experimental results show that MOMFS can effectively reduce the multi-label data dimension and perform better than other approaches on five evaluation criteria.

Feature selection plays a fundamental role in many data mining and machine learning tasks. In this paper, we proposed a novel feature selection method, namely, Dynamic Feature Selection Method with Minimum Redundancy Information (MRIDFS). In MRIDFS, the conditional mutual information is used to calculate the relevance and the redundancy among multiple features, and a new concept, the feature-dependent redundancy ratio, was introduced. Such ratio can represent redundancy more accurately. To evaluate our method, MRIDFS is tested and compared with seven popular methods on 16 benchmark data sets. Experimental results show that MRIDFS outperforms in terms of average classification accuracy.

Sum-rank Hamming codes are introduced in this work. They are essentially defined as the longest codes (thus of highest information rate) with minimum sum-rank distance at least $ 3 $ (thus one-error-correcting) for a fixed redundancy $ r $, base-field size $ q $ and field-extension degree $ m $ (i.e., number of matrix rows). General upper bounds on their code length, number of shots or sublengths and average sublength are obtained based on such parameters. When the field-extension degree is $ 1 $, it is shown that sum-rank isometry classes of sum-rank Hamming codes are in bijective correspondence with maximal-size partial spreads. In that case, it is also shown that sum-rank Hamming codes are perfect codes for the sum-rank metric. Also in that case, estimates on the parameters (lengths and number of shots) of sum-rank Hamming codes are given, together with an efficient syndrome decoding algorithm. Duals of sum-rank Hamming codes, called sum-rank simplex codes, are then introduced. Bounds on the minimum sum-rank distance of sum-rank simplex codes are given based on known bounds on the size of partial spreads. As applications, sum-rank Hamming codes are proposed for error correction in multishot matrix-multiplicative channels and to construct locally repairable codes over small fields, including binary.

In this paper, we focus on various compression and classification algorithms for three different paralinguistic signal classification tasks. These tasks are quite difficult for humans because the sound information from such signals is difficult to distinguish. Therefore, when machine learning techniques are applied to analyze paralinguistic signals, several different aspects of speech-related information, such as prosody, energy, and cepstral information, are usually considered for feature extraction. However, when the size of the training corpus is not sufficiently large, it is extremely difficult to directly apply machine learning to classify such signals due to their high feature dimensions; this problem is also known as the curse of dimensionality. This paper proposes to address this limitation by means of feature compression. First, we present experimental results obtained by using various compression algorithms to compress signals to eliminate redundancy of the signal features. We observe that compared with the original features, the compressed signal features still provide a comparable ability to distinguish the signals, especially when using a fully connected neural network classifier. Second, we calculate the output distribution of the F1-score for each emotion in the speech emotion recognition problem and show that the fully connected neural network classifier performs more stably than other classical methods.

In this paper, a computer based system has been proposed as a support to gastrointestinal polyp detection. It can detect and classify gastrointestinal polyps from endoscopic video. Color wavelet (CW) features and convolutional neural network (CNN) features of endoscopic video frames are extracted. Mutual information based feature selection technique-Minimum redundancy maximum relevance (mRMR) is used to scale down feature vector. Instead of using a single classifier, Bootstrap Aggregrating (Bagging)- an ensemble classifier is used. Proposed system has been assessed against different public databases and our own datasets. Evaluation shows that, the system outperforms the existing methods.

Intra prediction is an effective method for video coding to remove the spatial redundancy of content. Classical intra prediction method usually creates a prediction block by extrapolating the encoded pixels surrounding the target block. However, existing methods cannot guarantee the prediction efficiency for rich textural structure, especially when weak spatial correlation exists between the target block and reference pixels. To remedy this issue, this paper proposes a novel intra prediction method via convolutional encoder-decoder network, which we term IPCED. IPCED can learn and extract the internal representation of reference blocks, and progressively generate a prediction block from this representation. IPCED is a data-driven method, which represents an improvement over hand-crafted methods, and is capable of improving the accuracy of intra prediction. Extensive experimental results demonstrate that IPCED can generate higher-quality intra prediction results, achieves 3.41%, 3.07% and 3.44% bitrate saving for the Y/Cb/Cr channel compared with HEVC baseline, which is significantly beyond existing methods.

Aperture synthesis (AS) passive imaging technique has been proven effective in remote sensing for high resolution. Generally, a synthetic aperture radiometer needs the same number of channels as the antennas. As a consequence, the system complexity, volume, and cost increase rapidly as the size of the array expands. In this letter, the channel compressive AS (CCAS) method is proposed to reduce the receiver channels and correlators. Every channel connects to several different antennas by a selected connection network, and the visibilities are rebuilt from the cross correlation between output signals of the channels. Also, the principles of choosing connection network are discussed to guarantee the performance of the reconstructed brightness temperature (BT) images. Simulation results have shown the validation of the proposed method. It is of great application potential for very large-scale array in the future.

The wireless visual sensor network (WVSN) as a new emerged intelligent visual system, has been applied in many video monitoring sites. However, there is still great challenge because of the limited wireless network bandwidth. To resolve the problem, we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image frames. Firstly, an adaptively learning background model is improved to discover successive similar image frames from the inputting video sequence. Then, the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element dynamics. Furthermore, a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or not. To evaluate the trade-off performance of the proposed method, it is tested on the CDW-2014 dataset, which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes, and the misdetection rate of the undetected foreground frames is near to zero. Compared to the original stream, it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.

Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

The forensics of the median filtering is a challenging task due to its content preserving nature. Several methods have been proposed for median filtering forensics in digital images. However the performance of these methods deteriorates for compressed images, small resolutions of images and for anti-forensic operations. Moreover large feature set dimensions of these methods also pose a computational challenge. This paper proposes, a 8-dimensional feature set, derived from two state-of-the-art techniques by employing maximum relevancy and minimum redundancy (mRMR) feature selection approach. Features are selected by mRMR on the basis of distance correlation as an association measure. Extensive experiments are performed to evaluate the efficacy of proposed method through six different databases. The proposed method outperforms state-of-the-art techniques for uncompressed images, compressed images at low quality factors, low resolutions images and for an anti-forensic operation. The performance of the proposed method is also compared with convolutional neural network (CNN) based features for the detection of median filtering at low resolutions and for compressed images. Also, experimental results support the performance of proposed method over other manipulations (average and Gaussian filtering).

High dimensionality and sample imbalance of gene expression data promote the development of effective algorithms for classifying gene expression data. To improve the ability to distinguish different subtypes of gene expression data, we devise a hypervolume-based discrete evolutionary optimization algorithm (HYBDEOA) in this paper. Four objectives, namely the number of genes, the accuracy, the relevance, and the redundancy, are optimized simultaneously to guide the evolution. Firstly, binary encoding is used to choose some features, projecting data onto different subspaces. After that, a discrete neighborhood operation is conducted to generate a new binary-mapped population. Combining the new population with the current population, we employ the hypervolume-based mechanism to select the Pareto solutions. Finally, a discrete mutation method is proposed to find promising solutions in the binary search space. To demonstrate the performance of HYBDEOA, we apply HYBDEOA to 55 synthetic datasets and 35 cancer gene expression datasets. Extensive experiments are also conducted to reveal the effectiveness and efficiency of HYBDEOA. The experimental results demonstrate that our proposed method is a parameter-less and robust algorithm, which can group gene expression data with a finer and more informative classification.

The explosive growth of time-series data, the scale of time-series data (TSD) suggests that the scale and capability of many Internet of Things (IoT)-based applications has already been exceeded. Moreover, redundancy persists in TSD due to the correlation between information acquired via different sources. In this paper, we propose a cohort of dominant dataset selection algorithms for electricity consumption time-series data with a focus on discriminating the dominant dataset that is small dataset but capable of representing the kernel information carried by time-series data with an arbitrarily small error rate less than ". Furthermore, we prove that the selection problem of the minimum dominant dataset is an NP-complete problem. The affine transformation model is introduced to define the linear correlation relationship between time-series data objects. Our proposed framework consists of the scanning selection algorithm with O(n3) time complexity and the greedy selection algorithm with O(n4) time complexity, which are respectively proposed to select the dominant dataset based on the linear correlation distance between time-series data objects. The proposed algorithms are evaluated on the real electricity consumption data Harbin city in China. The experimental results show that the proposed algorithms not only reduce the size of the extracted kernel dataset but also ensure the time-series data integrity in terms of accuracy and efficiency., This paper has been accepted by IEEE Internet of Things Journal

Feature selection is an effective way to reduce the data dimensionality of hyperspectral imagery and obtain a better performance in the subsequent applications, such as classification. The ideal approach is to obtain the optimal tradeoff between two criteria for hyperspectral image feature selection: 1) information preservation and 2) redundancy reduction. However, constructing a hyperspectral feature selection model for the above two criteria is difficult due to the complexity of hyperspectral imagery. Although evolutionary multiobjective optimization methods have been recently presented to simultaneously optimize the above criteria, they cannot control the global exploration versus local exploitation capabilities in the search space for the hyperspectral feature selection problem. Thus, in this article, a novel discrete sine cosine algorithm (SCA)-based multiobjective feature selection (MOSCA_FS) approach is proposed for hyperspectral imagery. In the proposed method, a novel and effective framework of multiobjective hyperspectral feature selection is designed. In the framework, the ratio between the Jeffries–Matusita (JM) distance and mutual information (MI) is modeled to minimize the redundancy and maximize the relevance of the selected feature subset. In addition, another measurement—the variance (Var)—is applied for maximizing the information amount. Furthermore, to resolve the discrete hyperspectral feature selection problem, a novel discrete SCA is first proposed, which enhances the selection of the ideal feature subset. The effectiveness and universality of the proposed method was verified by experiments with ten University of California at Irvine (UCI) data sets, five hyperspectral image data sets, and one spectral data set of typical surface features.

Objectives The current study aims to determine the effect of physicochemical descriptor selection on models of polydimethylsiloxane permeation. Methods A total of 2942 descriptors were calculated for a data set of 77 chemicals. Data were processed to remove redundancy, single values, imbalanced and highly correlated data, yielding 1363 relevant descriptors. For four independent test sets, feature selection methods were applied and modelled via a variety of Machine Learning methods. Key findings Two sets of molecular descriptors which can provide improved predictions, compared to existing models, have been identified. Best permeation predictions were found with Gaussian Process methods. The molecular descriptors describe lipophilicity, partial charge and hydrogen bonding as key determinants of PDMS permeation. Conclusions This study highlights important considerations in the development of relevant models and in the construction and use of the data sets used in such studies, particularly that highly correlated descriptors should be removed from data sets. Predictive models are improved by the methodology adopted in this study, notably the systematic evaluation of descriptors, rather than simply using any and all available descriptors, often based empirically on in vitro experiments. Such findings also have clear relevance to a number of other fields.

Approaches to image compression with machine learning now achieve superior performance on the compression rate compared to existing hybrid codecs. The conventional learning-based methods for image compression exploits hyper-prior and spatial context model to facilitate probability estimations. Such models have limitations in modeling long-term dependency and do not fully squeeze out the spatial redundancy in images. In this paper, we propose a coarse-to-fine framework with hierarchical layers of hyper-priors to conduct comprehensive analysis of the image and more effectively reduce spatial redundancy, which improves the rate-distortion performance of image compression significantly. Signal Preserving Hyper Transforms are designed to achieve an in-depth analysis of the latent representation and the Information Aggregation Reconstruction sub-network is proposed to maximally utilize side-information for reconstruction. Experimental results show the effectiveness of the proposed network to efficiently reduce the redundancies in images and improve the rate-distortion performance, especially for high-resolution images. Our project is publicly available at https://huzi96.github.io/coarse-to-fine-compression.html.

Performance of the discrete Fourier transform (DFT)-based direction-of-arrival (DoA) estimation method depends on the number of antennas. Increasing the number of antennas will improve the accuracy of DoA estimation. In this study, the authors propose a new DFT-based DoA estimation method using a nested minimum redundancy array (NMRA). By vectorisation of the covariance matrix of the received signal of an NMRA with low number of antennas, a virtual uniform linear array with a large number of antennas can be obtained. An initial estimation of DoAs can be derived by applying the normalised centralised DFT matrix to the observations of virtual uniform linear array. By minimisation of the difference between the virtual linear array observations and their estimated values, the accuracy of DoA estimation will be improved. Simulation results show that the proposed method has better performance in comparison with the conventional ones.

A method to evaluate the mechanical performance of the corroded steel cable based on the strength condition is proposed. The corrosion safety factor (nα) was introduced, and the evaluation model was established. The eccentric effect as a result of the local corrosion was also considered. Then, the axial tensile experiment on steel-strand specimens was carried out. Furthermore, the mechanical properties, including the ultimate bearing capacity and limited strain, were analyzed. The results show that the properties of locally corroded specimens deteriorate more than uniformly corroded specimens. It is verified that the experimental data are in good agreement with the proposed theoretical model when the corrosion degree α is less than 0.35. Based on the experimental data and the related specification of the stayed cable, the relationship between nα and α was precisely determined. Besides, the level of nα or the safety redundancy can be characterized by the evaluation score δ. The lower the score δ, the less safe is the cable structure. This method also provides significant application prospects for safety evaluation research in civil engineering.

In the face of rapid urbanization, monitoring urban expansion has gained importance to sustainably manage the land resources and minimize the impact on the environment. Monitoring urban growth using satellite data involves computing the state of land use–land cover and their change over time. A number of computing methods have been developed to process and interpret the satellite results for an urban environment. However, due to a large number of parametric and nonparametric algorithms used for land-use–land-cover classification, there is uncertainty regarding choosing the best algorithm to measure the urban processes. In this study, several parametric (maximum likelihood) and nonparametric (support vector machine, spectral angle mapper, artificial neural network and decision tree) algorithms were used. The study was aimed at finding out the best available classification technique for land-use–land-cover classification and change detection. Landsat 8, the latest in Landsat series, and Landsat 7 and 5 freely available satellite data were used. Due to the redundancy reported for the traditional kappa-based indices, we applied modern disagreement indices to assess the accuracy of the classification process. Artificial neural network for Landsat 8 image had the highest kappa coefficient, while spectral angle mapper had the highest overall agreement (97%) and least quantity allocation error (1%). Spectral Angle Mapper gave the highest accuracy, while maximum likelihood classification gave the least for allocation and spatial disagreement indices. We found that spectral angle mapper gave the best results for land-use–land-cover change analysis in terms of least omission and commission errors (2.5% each) and highest overall agreement, whereas artificial neural network performed better in land-use–land-cover classification studies.

Motivation The Protein Data Bank (PDB), the ultimate source for data in structural biology, is inherently imbalanced. To alleviate biases, virtually all structural biology studies use nonredundant (NR) subsets of the PDB, which include only a fraction of the available data. An alternative approach, dubbed redundancy-weighting (RW), down-weights redundant entries rather than discarding them. This approach may be particularly helpful for machine-learning (ML) methods that use the PDB as their source for data. Methods for secondary structure prediction (SSP) have greatly improved over the years with recent studies achieving above 70% accuracy for eight-class (DSSP) prediction. As these methods typically incorporate ML techniques, training on RW datasets might improve accuracy, as well as pave the way toward larger and more informative secondary structure classes. Results This study compares the SSP performances of deep-learning models trained on either RW or NR datasets. We show that training on RW sets consistently results in better prediction of 3- (HCE), 8- (DSSP) and 13-class (STR2) secondary structures. Availability and implementation The ML models, the datasets used for their derivation and testing, and a stand-alone SSP program for DSSP and STR2 predictions, are freely available under LGPL license in http://meshi1.cs.bgu.ac.il/rw. Supplementary information Supplementary data are available at Bioinformatics online.

The literature showing that subscores fail to add value is vast; yet despite their typical redundancy and the frequent presence of substantial statistical errors, many stakeholders remain convinced of their necessity. This article describes a method for identifying and reporting unexpectedly high or low subscores by comparing each examinee’s observed subscore with a discrete probability distribution of subscores conditional on the examinee’s overall ability. The proposed approach turns out to be somewhat conservative due to the nature of subscores as finite sums of item scores associated with a subdomain. Thus, the method may be a compromise that satisfies score users by reporting subscore information as well as psychometricians by limiting misinterpretation, at most, to the rates of Type I and Type II error.

The fundamental problem in information and communication theory is how to achieve reliable communication over channels that are originally unreliable in a sense that each individual transmission over the channel has the chance to be received incorrectly. The way to improve the reliability of the individual data bits is to use redundancy in a form of a forward error correction (FEC) code. This chapter begins by introducing the methods for error correction through some simple examples involving the binary symmetric channel. It explains how to select as few inputs as possible in order to guarantee high reliability for the transmission of a single input in the expanded channel. The chapter also discusses an important class of coding instruction, the linear block codes. In order to design a transmission mode with FEC coding and a specific modulation, we can apply a layered approach for the Gaussian channel, that is, the coding and modulation are designed separately.

In this letter, we propose a concise feature representation framework for acoustic scene classification by pruning embeddings obtained from SoundNet, a deep convolutional neural network. We demonstrate that the feature maps generated at various layers of SoundNet have redundancy. The proposed singular value decomposition based method reduces the redundancy while relying on the assumption that useful feature maps produced by different classes lie along different directions. This leads to ignoring the feature maps that produce similar embeddings for different classes. In the context of using an ensemble of classifiers on the various layers of SoundNet, pruning the redundant feature maps leads to reduction in dimensionality and computational complexity. Our experiments on acoustic scene classification demonstrate that ignoring 73% of feature maps reduces the performance by less than 1% with 12.67% reduction in computational complexity. In addition to this, we also show that the proposed pruning framework can be utilized to remove filters in the SoundNet network architecture, with 13x lesser model storage requirement. Also, the number of parameters reduce from 28 million to 2 million with marginal degradation in performance. This small model obtained after applying the proposed pruning procedure is evaluated on different acoustic scene classification datasets, and shows excellent generalization ability.

The problem of designing a fixed code for encoding all monotone sources with a large alphabet size $n$ is studied. It is proved that if the codelengths of a code sequence are of order $O(\log n)$ , then the redundancy for almost all monotone sources with $n$ symbols is very close to that for a specific distribution, so-called average distribution. Noting this point, for any large alphabet size $n$ , a new code with a very simple structure is proposed whose redundancy is close to that of the Huffman code for almost all monotone sources with alphabet size $n$ .

In category-theoretic models for the anyon systems proposed for topological quantum computing, the essential ingredients are two monoidal structures, $\oplus$ and $\otimes$. The former is symmetric but the latter is only braided, and $\otimes$ is required to distribute over $\oplus$. What are the appropriate coherence conditions for the distributivity isomorphisms? We came to this question working on a simplification of the category-theoretical foundation of topological quantum computing, which is the intended application of the research reported here. This question was answered by Laplaza when both monoidal structures are symmetric, but topological quantum computation depends crucially on $\otimes$ being only braided, not symmetric. We propose coherence conditions for distributivity in this situation, and we prove that our conditions are (a) strong enough to imply Laplaza's when the latter are suitably formulated, and (b) weak enough to hold when --- as in the categories used to model anyons --- the additive structure is that of an abelian category and the braided $\otimes$ is additive. Working on these results, we found a new redundancy in Laplaza's conditions., This is a companion paper for article "Witness algebra and anyon braiding," arXiv:1807.10414, proving results used there

The real world data are complex in nature and addition to that a large number of features add more value to the complexity. However, the features associated with the data may be redundant and erroneous in nature. To deal with such type of features, feature selection plays a vital role in computational learning. The reduction in the dimensionality of the dataset not only reduces the computational time required for classification but also enhances the classification accuracy by removing the misleading features. This paper presents a Filter Approach using Elitism based Multi-objective Differential Evolution algorithm for feature selection (FAEMODE) and the novelty lies in the objective formulation, where both linear and nonlinear dependency among features have been considered to handle the redundant and unwanted features of a dataset. Finally, the selected feature subsets of 23 benchmark datasets are tested using 10-fold cross validation with four well-known classifiers to endorse the result. A comparative analysis of the proposed approach with seven filter approaches and two conventional as well as three metaheuristic based wrapper approaches have been carried out for validation. The result reveals that the proposed approach can be considered as a powerful filter method for feature selection in various fields. Keywords: Multi-objective, Feature selection, Differential Evolution, Filter approach, Correlation coefficient, Mutual information

This article proposes a functional data discriminant analysis (FDDA) method for hyperspectral image (HSI) classification. This method analyzes and processes the HSI data from a functional point of view, which is a novel perspective in HSI processing. The classical methods achieve dimensionality reduction by directly eliminating the redundancy of the HSI data. However, the proposed method extracts the functional features by utilizing the redundancy of the HSI data. Functional features can effectively reveal inherent characteristics of the HSI data with the change in the wavelengths. Based on this, a regularized weighted fitting model is first built for converting a spectral vector into a spectral curve. Second, an FDDA method defined in the function field is presented for extracting the functional features of the spectral curves. Finally, a novel spectral–spatial framework is designed for classification tasks of HSI data sets. Experimental results in three commonly used HSI data sets indicate that the proposed method is effective and leads to promising classification results compared with some benchmarking methods. More importantly, the work tries to diversify and develop the existing theory and methods of HSI classification from discrete (vector) data learning methods to continuous (functional) data learning methods.

Permutation codes and multipermutation codes are widely studied due to various applications in information theory. Designing codes correcting deletion errors has been the main subject of works in the literature and to the best of our knowledge, there exist only optimal codes capable of correcting a single deletion in a permutation. In this paper, we construct several classes of permutation and multipermutation codes that are capable of correcting a burst deletion of length $s \geq 2$ , for both stable and unstable models. Efficient error decoders are provided to show the correctness of our constructions.

In this article, a new methodology for the centroid variation of chaos self-synchronization error dynamics was used to determine soil salinization degree based on probability neural network (PNN). This was done to overcome the difficulties involved in the handling of a large amount of spectroscopy data, as well as many spectral bands and a low determination rate caused by bands redundancy. The spectral reflectance of saline soils in Xinjiang Uygur Autonomous Region was used as data sources. The results showed that salinization in the area was severe. The proportion of saline, moderately saline, and severely saline soil accounted for 67.3% of the total sample. Fractional-order master/slave chaotic analysis was carried out on the characteristics of soil spectroscopy data with different salinization degrees. The differences between integer order and fractional order of chaotic dynamic error were compared. Simulation results showed that changes in the 0.6 order chaos dynamic error were the most significant and so these were used as PNN input vectors. The PNN model was used to identify the nonlinear hyperspectral signal of soil salinization degrees after chaotic system conversion. The input vector was normalized after insertion into the PNN model input layer and was added to the hidden layer for Gaussian operations. Finally, the hidden layer results were used in the summation layer to calculate the correlation. The verification set classification result was 93.5%. The studies showed that the method proposed in this article could serve as a new way for classifying soil salinization, which has a classification accuracy of 93.5%, and the soil salinization degree can be rapidly determined.

This paper introduces a new compression scheme that exploits temporal redundancies in video segments utilizing the pixogram concept recently introduced in the literature. By taking into consideration the correlation between video pixels along the time domain, a pixogram has the ability to transform uncorrelated spatial areas of separate video-frames into highly correlated temporal vectors, thus increasing redundancy in the transform domain. This strategy allows for higher compression ratios in video streams. The proposed compression scheme is the first compression technique that utilizes the pixogram concept in video compression, and aims to challenge the traditional trade-off associated with high compression ratios leading to reduced visual quality. Comparisons with popular compression standards demonstrate the advantage this new approach has for highly correlated video segments. Moreover, the proposed technique is more suitable to parallelization, and thus outperforms other compression techniques in terms of execution time.

Lossy /lossless image compression technique results will either improve the quality of image with reduced compression ratio or degrades the image with better compression ratio. But on dealing with the combined lossy/lossless techniques, they provide the output with better image quality and high compression rate, however, the techniques failed to obtain better efficiency due to less predictive nature. Hence, to acquire an image having better compression rate with improved predictive nature, a novel framework is introduced in our proposed work. It uses a Deep Residual Network Bi-Layer Enfolded Compressor (DRN-BLEC) to enhance the image quality. In order to obtain better compression rate, the Mexican Meyer Hat Wavelet Transform (MMHWT) is used. Similarly, the well knowledge quantization is achieved by utilizing DRN for learning; it is then followed by the Lloyds quantization technique that groups the feature values by quantizing it, which in turn boost-up the resolution of the image. Consequently with DRN, the process of encoding and decoding doesn’t requires to be done separately since two residual layer of the DRN are trained to encode and decode the images, which thereby reduces the time required for compression process. Thus a compressed image with high resolution is obtained without redundancy, moreover with high compression ratio. The architecture of the DRN-BLEC is implemented in MATLAB and the respective result structure is validated.

Feature selection plays a critical role in many applications that are relevant to machine learning, image processing and gene expression analysis. Traditional feature selection methods intend to maximize feature dependency while minimizing feature redundancy. In previous information-theoretical-based feature selection methods, feature redundancy term is measured by the mutual information between a candidate feature and each already-selected feature or the interaction information among a candidate feature, each already-selected feature and the class. However, the larger values of the traditional feature redundancy term do not indicate the worse a candidate feature because a candidate feature can obtain large redundant information, meanwhile offering large new classification information. To address this issue, we design a new feature redundancy term that considers the relevancy between a candidate feature and the class given each already-selected feature, and a novel feature selection method named min-redundancy and max-dependency (MRMD) is proposed. To verify the effectiveness of our method, MRMD is compared to eight competitive methods on an artificial example and fifteen real-world data sets respectively. The experimental results show that our method achieves the best classification performance with respect to multiple evaluation criteria.