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Robust sparse coding via self-paced learning for data representation

INFORMATION SCIENCES 2021年 546卷 448-468页

作者： Feng, Xiaodong Wu, Sen Univ Elect Sci & Technol China Sch Publich Affairs & Adm Dept Informat Management Chengdu 611731 Sichuan Peoples R China Univ Sci & Technol Beijing Sch Econ & Management Beijing 100083 Peoples R China

sparse coding (SC), due to its thorough theoretical property and outstanding effectiveness, is attracting more and more attention in various data representation and data mining applications. However, the optimization of most existing sparse coding algorithms are non-convex and thus prone to become stuck into bad local minima under the framework of alternative optimization, especially when there are many outliers and noisy data. To enhance the learning robustness, in this study, we will present an unified framework named Self-Paced sparse coding (SPSC), which gradually includes data into the learning process of SC from easy ones to complex ones by incorporating self-paced learning methodology. It implements a soft instance selection accordingly rather than a heuristic hard strategy sample selection. We also generalize the self-paced learning schema into different levels of dynamic selection on instances, features and elements respectively. Further, we show an optimization algorithm to solve it and a theoretical explanation to analyze the effectiveness of it. Extensive experimental results on the real-world clean image datasets and images with two kinds of corruptions demonstrate the remarkable robustness of the proposed method for high dimensional data representation on image clustering and reconstruction tasks over the state-of-the-arts. (c) 2020 Elsevier Inc. All rights reserved.

关键词： Data representation sparse coding Non-convex optimization Self-paced learning Noisy corruption

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An Efficient Edge sparse coding Approach to Ultra-Short-Term Household Electricity Demand Estimation

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2018年第11期13卷 1586-1594页

作者： Sun, Yi Liu, Yaoxian Zhang, Lu Cao, Yongfeng Zhao, Xiongwen North China Elect Power Univ Sch Elect & Elect Engn BeiNong Rd 2 Beijing 102206 Peoples R China

With the opening of electricity market, the interaction between grids and users is becoming more and more frequent. Household electricity demand estimation is a significant and indispensable process of the necessary precise demand response in the future. Large-scale coverage of the Advanced Metering Infrastructure provides a large volume of user electricity data and brings opportunities for residential electricity consumption forecasting, but, on the other hand, it has brought tremendous pressure on the communication link and data computing center. This paper proposes an efficient edge sparse coding method based on the K-singular value decomposition (K-SVD) algorithm to extract hidden usage behavior patterns (UBPs) from load datasets and reduce the cost of communication, storage, and computation. The load of representative household appliances is introduced as the initial dictionary of the K-SVD algorithm in order to make the UBPs more proximate to the residents' daily electricity consumption. Then, a linear support vector machine (SVM)-based method with UBPs is used to predict the subsequent interval household electricity demand. The experimental result shows that the proposed algorithm can effectively follow the trend of the real load curve and realize accurate forecasting of the peak electricity demand. (C) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： ultra-short-term electricity demand forecasting sparse coding smart meters patterns extraction non-intrusive load monitoring

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Adaptive feature extraction using sparse coding for machinery fault diagnosis

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2011年第2期25卷 558-574页

作者： Liu, Haining Liu, Chengliang Huang, Yixiang Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China

In the signal processing domain, there has been growing interest in sparse coding with a learned dictionary instead of a predefined one, which is advocated as an effective mathematical description for the underlying principle of mammalian sensory systems in processing information. In this paper, sparse coding is introduced as a feature extraction technique for machinery fault diagnosis and an adaptive feature extraction scheme is proposed based on it. The two core problems of sparse coding, i.e., dictionary learning and coefficients solving, are discussed in detail. A natural extension of sparse coding, shift-invariant sparse coding, is also introduced. Then, the vibration signals of rolling element bearings are taken as the target signals to verify the proposed scheme, and shift-invariant sparse coding is used for vibration analysis. With the purpose of diagnosing the different fault conditions of bearings, features are extracted following the proposed scheme: basis functions are separately learned from each class of vibration signals trying to capture the defective impulses;a redundant dictionary is built by merging all the learned basis functions;based on the redundant dictionary, the diagnostic information is made explicit in the solved sparse representations of vibration signals;sparse features are formulated in terms of activations of atoms. The multiclass linear discriminant analysis (LDA) classifier is used to test the discriminability of the extracted sparse features and the adaptability of the learned atoms. The experiments show that sparse coding is an effective feature extraction technique for machinery fault diagnosis. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： sparse coding Shift-invariant sparse coding Vibration analysis Feature extraction Fault diagnosis

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Deep Neural Network Structured sparse coding for Online Processing

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IEEE ACCESS 2018年 6卷 74778-74791页

作者： Zhao, Haoli Ding, Shuxue Li, Xiang Huang, Huakun Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu Fukushima 9650005 Japan

sparse coding, which aims at finding appropriate sparse representations of data with an overcomplete dictionary set, has become a mature class of methods with good efficiency in various areas, but it faces limitations in immediate processing such as real-time video denoising. Unsupervised deep neural network structured sparse coding (DNN-SC) algorithms can enhance the efficiency of iterative sparse coding algorithms to achieve the goal. In this paper, we first propose a sparse coding algorithm by adding the idea "weighted" in the iterative shrinkage thresholding algorithm (ISTA), named WISTA, which can enjoy the benefit of the l(p) norm (0 < p < 1) sparsity constraint. Then, we propose two novel DNN-SC algorithms by combining deep learning with WISTA and the iterative half thresholding algorithm (IHTA), which is the 10.5 norm sparse coding algorithm. Furthermore, we present that by changing the loss function, the DNN can be learned supervisedly and unsupervisedly. Unsupervised learning is the key to ensure the DNN to be learned online during processing, which enables the use of the DNN-SC algorithms in applications lacking labels for signals. Synthetic data experiments show that WISTA can outperform ISTA and IHTA. Moreover, the DNN-structured WISTA can successfully achieve converged results of WISTA. In real-world data experiments, the procedure of utilizing DNN-SC algorithms in image denoising is first presented. All DNN-SC algorithms can accelerate at least 45 times while maintaining PSNR results compared with their corresponding sparse coding algorithms. Finally, the strategy of utilizing DNN-SC algorithms in real-time video denoising is presented. The video-denoising experiments show that the DNN-structured ISTA and WISTA can conduct real-time video denoising for 25 frames/s 360 x 480 pixels gray-scaled videos.

关键词： sparse coding deep neural network weighted iterative shrinkage thresholding algorithm unsupervised learning real-time video denoising

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Multi-frame image super-resolution reconstruction via low-rank fusion combined with sparse coding

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第6期78卷 7143-7154页

作者： Zhu, Xuan Jin, Peng Wang, XianXian Ai, Na Northwest Univ Sch Informat Sci & Technol Xian Shaanxi Peoples R China

The sparse coding method has been successfully applied to multi-frame super-resolution in recent years. In this paper, we propose a new multi-frame super-resolution framework which combines low-rank fusion with sparse coding to improve the performance of multi-frame super-resolution. The proposed method gets the high-resolution image by a three-stage process. First, a fused low-resolution image is obtained from multi-frame image by the method of registration and low-rank fusion. Then, we use the jointly training method to train a pair of learning dictionaries which have good adaptive ability. Finally, we use the learning dictionaries combined with sparse coding theory to realize super-resolution reconstruction of the fused low-resolution image. As the experiment results show, this method can recover the lost high frequency information, and has good robustness.

关键词： Super-resolution reconstruction Multi-frame image Low-rank fusion sparse coding

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Statistically Adaptive Image Denoising Based on Overcomplete Topographic sparse coding

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NEURAL PROCESSING LETTERS 2015年第3期41卷 357-369页

作者： Zhao, Haohua Luo, Jun Huang, Zhiheng Nagumo, Takefumi Murayama, Jun Zhang, Liqing Shanghai Jiao Tong Univ Dept Comp Sci & Engn Key Lab Shanghai Educ Commiss Intelligent Interact & Cogn Shanghai 200030 Peoples R China Sony Corp Tokyo Japan

This paper presents a novel image denoising framework using overcomplete topographic model. To adapt to the statistics of natural images, we impose both spareseness and topograpgic constraints on the denoising model. Based on the overcomplete topographic model, our denoising system improves the previous work on the following aspects: multi-category based sparse coding, adaptive learning, local normalization, lasso shrinkage function, and subset selection. A large number of simulations have been performed to show the performance of the modified model, demonstrating that the proposed model achieves better denoising performance.

关键词： Overcomplete sparse coding Topographic constraints Image denoising Multi-category Adaptive learning Shrinkage

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Shift ∧ 2D Rotation Invariant sparse coding for Multivariate Signals

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2012年第4期60卷 1597-1611页

作者： Barthelemy, Quentin Larue, Anthony Mayoue, Aurelien Mercier, David Mars, Jerome I. CEA LIST Data Anal Tools Lab F-91191 Gif Sur Yvette France GIPSA Lab F-38402 St Martin Dheres France

Classical dictionary learning algorithms (DLA) allow unicomponent signals to be processed. Due to our interest in two-dimensional (2D) motion signals, we wanted to mix the two components to provide rotation invariance. So, multicomponent frameworks are examined here. In contrast to the well-known multichannel framework, a multivariate framework is first introduced as a tool to easily solve our problem and to preserve the data structure. Within this multivariate framework, we then present sparse coding methods: multivariate orthogonal matching pursuit (M-OMP), which provides sparse approximation for multivariate signals, and multivariate DLA (M-DLA), which empirically learns the characteristic patterns (or features) that are associated to a multivariate signals set, and combines shift-invariance and online learning. Once the multivariate dictionary is learned, any signal of this considered set can be approximated sparsely. This multivariate framework is introduced to simply present the 2D rotation invariant (2DRI) case. By studying 2D motions that are acquired in bivariate real signals, we want the decompositions to be independent of the orientation of the movement execution in the 2D space. The methods are thus specified for the 2DRI case to be robust to any rotation: 2DRI-OMP and 2DRI-DLA. Shift and rotation invariant cases induce a compact learned dictionary and provide robust decomposition. As validation, our methods are applied to 2D handwritten data to extract the elementary features of this signals set, and to provide rotation invariant decomposition.

关键词： Dictionary learning algorithm handwritten data multichannel multivariate online learning orthogonal matching pursuit rotation invariant shift-invariant sparse coding trajectory characters

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Risk bound of transfer learning using parametric feature mapping and its application to sparse coding

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MACHINE LEARNING 2019年第11期108卷 1975-2008页

作者： Kumagai, Wataru Kanamori, Takafumi RIKEN Ctr Adv Intelligence Project Chuo Ku 1-4-1 Nihonbashi Tokyo 1030027 Japan Tokyo Inst Technol Dept Math & Comp Sci Meguro Ku 2-12-1 Ookayama Tokyo 1528552 Japan

In this study, we consider a transfer-learning problem using the parameter transfer approach, in which a suitable parameter of feature mapping is learned through one task and applied to another objective task. We introduce the notion of local stability and parameter transfer learn-ability of parametric feature mapping, and derive an excess risk bound for parameter transfer algorithms. As an application of parameter transfer learning, we discuss the performance of sparse coding in self-taught learning. Although self-taught learning algorithms with a large volume of unlabeled data often show excellent empirical performance, their theoretical analysis has not yet been studied. In this paper, we also provide a theoretical excess risk bound for self-taught learning. In addition, we show that the results of numerical experiments agree with our theoretical analysis.

关键词： Transfer learning sparse coding Risk bound

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Image encryption using sparse coding and compressive sensing

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2019年第4期30卷 1895-1909页

作者： Ponuma, R. Amutha, R. Sri Sivasubramaniya Nadar Coll Engn Dept ECE Chennai Tamil Nadu India

An encryption algorithm based on sparse coding and compressive sensing is proposed. sparse coding is used to find the sparse representation of images as a linear combination of atoms from an overcomplete learned dictionary. The overcomplete dictionary is learned using K-SVD, utilizing non-overlapping patches obtained from a set of images. Compressed sensing is used to sample data at a rate below the Nyquist rate. A Gaussian measurement matrix compressively samples the plain image. As these measurements are linear, chaos based permutation and substitution operations are performed to obtain the cipher image. Bit-level scrambling and block substitution is done to confuse and diffuse the measurements. Simulation results verify the performance of the proposed technique against various statistical attacks.

关键词： Chaotic map Compressive sensing Dictionary learning Encryption Measurement matrix sparse coding

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Combining Non-negative Matrix Factorization and sparse coding for Functional Brain Overlapping Community Detection

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COGNITIVE COMPUTATION 2018年第6期10卷 991-1005页

作者： Li, X. Hu, Z. Wang, H. Foshan Univ Sch Math & Big Data Foshan 528000 Guangdong Peoples R China Southeast Univ Res Ctr Learning Sci Nanjing 210096 Jiangsu Peoples R China Anhui Univ Technol Sch Math & Phys Maanshan 243002 Anhui Peoples R China

The functional system of the human brain can be viewed as a complex network. Among various features of the brain functional network, community structure has raised significant interest in recent years. Increasing evidence has revealed that most realistic complex networks have an overlapping community structure. However, the overlapping community structure of the brain functional network has not been adequately studied. In this paper, we propose a novel method called sparse symmetric non-negative matrix factorization (ssNMF) to detect the overlapping community structure of the brain functional network. Specifically, it is formulated by combining the effective techniques of non-negative matrix factorization and sparse coding. Besides, the non-negative adaptive sparse representation is applied to construct the whole-brain functional network, based on which ssNMF is performed to detect the community structure. Both simulated and real functional magnetic resonance imaging data are used to evaluate ssNMF. The experimental results demonstrate that the proposed ssNMF method is capable of accurately and stably detecting the underlying overlapping community structure. Moreover, the physiological interpretation of the overlapping community structure detected by ssNMF is straightforward. This novel framework, we think, provides an effective tool to study overlapping community structure and facilitates the understanding of the network organization of the functional human brain.

关键词： Overlapping community detection Non-negative matrix factorization sparse coding Brain functional network Functional magnetic resonance imaging

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