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Journal articles on the topic 'Sparse and low-rank decomposition'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Ong, Frank, and Michael Lustig. "Beyond Low Rank + Sparse: Multiscale Low Rank Matrix Decomposition." IEEE Journal of Selected Topics in Signal Processing 10, no. 4 (2016): 672–87. http://dx.doi.org/10.1109/jstsp.2016.2545518.

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Yin, Jingwei, Bing Liu, Guangping Zhu, and Zhinan Xie. "Moving Target Detection Using Dynamic Mode Decomposition." Sensors 18, no. 10 (2018): 3461. http://dx.doi.org/10.3390/s18103461.

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It is challenging to detect a moving target in the reverberant environment for a long time. In recent years, a kind of method based on low-rank and sparse theory was developed to study this problem. The multiframe data containing the target echo and reverberation are arranged in a matrix, and then, the detection is achieved by low-rank and sparse decomposition of the data matrix. In this paper, we introduce a new method for the matrix decomposition using dynamic mode decomposition (DMD). DMD is usually used to calculate eigenmodes of an approximate linear model. We divided the eigenmodes into

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3

Rahmani, Mostafa, and George K. Atia. "High Dimensional Low Rank Plus Sparse Matrix Decomposition." IEEE Transactions on Signal Processing 65, no. 8 (2017): 2004–19. http://dx.doi.org/10.1109/tsp.2017.2649482.

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4

Chartrand, R. "Nonconvex Splitting for Regularized Low-Rank + Sparse Decomposition." IEEE Transactions on Signal Processing 60, no. 11 (2012): 5810–19. http://dx.doi.org/10.1109/tsp.2012.2208955.

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5

Liu, Jingjing, Donghui He, Xiaoyang Zeng, et al. "ManiDec: Manifold Constrained Low-Rank and Sparse Decomposition." IEEE Access 7 (2019): 112939–52. http://dx.doi.org/10.1109/access.2019.2935235.

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6

Rong, Kaixuan, Licheng Jiao, Shuang Wang, and Fang Liu. "Pansharpening Based on Low-Rank and Sparse Decomposition." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 12 (2014): 4793–805. http://dx.doi.org/10.1109/jstars.2014.2347072.

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7

Zhao, Jingtao, Caixia Yu, Suping Peng, and Chuangjian Li. "3D diffraction imaging method using low-rank matrix decomposition." GEOPHYSICS 85, no. 1 (2020): S1—S10. http://dx.doi.org/10.1190/geo2018-0417.1.

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Seismic weak responses from subsurface small-scale geologic discontinuities or inhomogeneities are encoded in 3D diffractions. Separating weak diffractions from a strong reflection background is a difficult problem for diffraction imaging, especially for the 3D case when they are tangent to or interfering with each other. Most conventional diffraction separation methods ignore the azimuth discrepancy between reflections and diffractions when suppressing reflections. In fact, the reflections associated with a specific pair of azimuth-dip angle possess sparse characteristics, and the diffraction

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8

Huang, Jianjun, Xiongwei Zhang, Yafei Zhang, Xia Zou, and Li Zeng. "Speech Denoising via Low-Rank and Sparse Matrix Decomposition." ETRI Journal 36, no. 1 (2014): 167–70. http://dx.doi.org/10.4218/etrij.14.0213.0033.

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9

Zhang, He, and Vishal M. Patel. "Convolutional Sparse and Low-Rank Coding-Based Image Decomposition." IEEE Transactions on Image Processing 27, no. 5 (2018): 2121–33. http://dx.doi.org/10.1109/tip.2017.2786469.

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10

Gong, Wenyong, Weihong Xu, Leqin Wu, Xiaohua Xie, and Zhanglin Cheng. "Intrinsic Image Sequence Decomposition Using Low-Rank Sparse Model." IEEE Access 7 (2019): 4024–30. http://dx.doi.org/10.1109/access.2018.2888946.

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11

Li, Junxia, Jian Yang, Chen Gong, and Qingshan Liu. "Saliency fusion via sparse and double low rank decomposition." Pattern Recognition Letters 107 (May 2018): 114–22. http://dx.doi.org/10.1016/j.patrec.2017.08.014.

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12

Li, Chunlei, Chaodie Liu, Zhoufeng Liu, Ruimin Yang, and Yun Huang. "Fabric defect detection method based on cascaded low-rank decomposition." International Journal of Clothing Science and Technology 32, no. 4 (2020): 483–98. http://dx.doi.org/10.1108/ijcst-03-2019-0037.

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PurposeThe purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.Design/methodology/approachThis paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the se

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13

Zhou, Junxiu, Yangyang Tao, and Xian Liu. "Tensor Decomposition for Salient Object Detection in Images." Big Data and Cognitive Computing 3, no. 2 (2019): 33. http://dx.doi.org/10.3390/bdcc3020033.

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The fundamental challenge of salient object detection is to find the decision boundary that separates the salient object from the background. Low-rank recovery models address this challenge by decomposing an image or image feature-based matrix into a low-rank matrix representing the image background and a sparse matrix representing salient objects. This method is simple and efficient in finding salient objects. However, it needs to convert high-dimensional feature space into a two-dimensional matrix. Therefore, it does not take full advantage of image features in discovering the salient object

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14

Yang, Bo, Kunkun Tong, Xueqing Zhao, Shanmin Pang, and Jinguang Chen. "Multilabel Classification Using Low-Rank Decomposition." Discrete Dynamics in Nature and Society 2020 (April 7, 2020): 1–8. http://dx.doi.org/10.1155/2020/1279253.

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In the multilabel learning framework, each instance is no longer associated with a single semantic, but rather with concept ambiguity. Specifically, the ambiguity of an instance in the input space means that there are multiple corresponding labels in the output space. In most of the existing multilabel classification methods, a binary annotation vector is used to denote the multiple semantic concepts. That is, +1 denotes that the instance has a relevant label, while −1 means the opposite. However, the label representation contains too little semantic information to truly express the difference

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15

Chandrasekaran, Venkat, Sujay Sanghavi, Pablo A. Parrilo, and Alan S. Willsky. "Sparse and Low-Rank Matrix Decompositions." IFAC Proceedings Volumes 42, no. 10 (2009): 1493–98. http://dx.doi.org/10.3182/20090706-3-fr-2004.00249.

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16

Zheng, Cheng Yong, and Hong Li. "Small Infrared Target Detection Based on Low-Rank and Sparse Matrix Decomposition." Applied Mechanics and Materials 239-240 (December 2012): 214–18. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.214.

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Sparse and low-rank matrix decomposition (SLMD) tries to decompose a matrix into a low-rank matrix and a sparse matrix, it has recently attached much research interest and has good applications in many fields. An infrared image with small target usually has slowly transitional background, it can be seen as the sum of low-rank background component and sparse target component. So by SLMD, the sparse target component can be separated from the infrared image and then be used for small infrared target detection (SITD). The augmented Lagrange method, which is currently the most efficient algorithm u

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17

Sun, Lijuan, Songhe Feng, Tao Wang, Congyan Lang, and Yi Jin. "Partial Multi-Label Learning by Low-Rank and Sparse Decomposition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5016–23. http://dx.doi.org/10.1609/aaai.v33i01.33015016.

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Multi-Label Learning (MLL) aims to learn from the training data where each example is represented by a single instance while associated with a set of candidate labels. Most existing MLL methods are typically designed to handle the problem of missing labels. However, in many real-world scenarios, the labeling information for multi-label data is always redundant , which can not be solved by classical MLL methods, thus a novel Partial Multi-label Learning (PML) framework is proposed to cope with such problem, i.e. removing the the noisy labels from the multi-label sets. In this paper, in order to

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18

Liu, Xin, Guoying Zhao, Jiawen Yao, and Chun Qi. "Background Subtraction Based on Low-Rank and Structured Sparse Decomposition." IEEE Transactions on Image Processing 24, no. 8 (2015): 2502–14. http://dx.doi.org/10.1109/tip.2015.2419084.

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19

Wang, Yishu, Dejie Yang, and Minghua Deng. "Low-Rank and Sparse Matrix Decomposition for Genetic Interaction Data." BioMed Research International 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/573956.

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Background. Epistatic miniarray profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. One approach to analyze EMAP data is to identify gene modules with densely interacting genes. In addition, genetic interaction score (Sscore) reflects the degree of synergizing or mitigating effect of two mutants, which is also informative. Statistical approaches that exploit both modularity and the pairwise interactions may provide more insight into the underlying biology. However, the high missing rate in EMAP data

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20

Zarmehi, Nematollah, Arash Amini, and Farokh Marvasti. "Low Rank and Sparse Decomposition for Image and Video Applications." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 7 (2020): 2046–56. http://dx.doi.org/10.1109/tcsvt.2019.2923816.

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21

Hou, Thomas Y., Qin Li, and Pengchuan Zhang. "A Sparse Decomposition of Low Rank Symmetric Positive Semidefinite Matrices." Multiscale Modeling & Simulation 15, no. 1 (2017): 410–44. http://dx.doi.org/10.1137/16m107760x.

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22

Tan, Jiaju, Qili Zhao, Xuemei Guo, Xin Zhao, and Guoli Wang. "Radio Tomographic Imaging Based on Low-Rank and Sparse Decomposition." IEEE Access 7 (2019): 50223–31. http://dx.doi.org/10.1109/access.2019.2910607.

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23

Zisheng Liu, Jicheng Li, Guo Li, Jianchao Bai, and Xuenian Liu. "A NEW MODEL FOR SPARSE AND LOW-RANK MATRIX DECOMPOSITION." Journal of Applied Analysis & Computation 7, no. 2 (2017): 600–616. http://dx.doi.org/10.11948/2017037.

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24

Nazari Siahsar, Mohammad Amir, Saman Gholtashi, Amin Roshandel Kahoo, Hosein Marvi, and Alireza Ahmadifard. "Sparse time-frequency representation for seismic noise reduction using low-rank and sparse decomposition." GEOPHYSICS 81, no. 2 (2016): V117—V124. http://dx.doi.org/10.1190/geo2015-0341.1.

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Attenuation of random noise is a major concern in seismic data processing. This kind of noise is usually characterized by random oscillation in seismic data over the entire time and frequency. We introduced and evaluated a low-rank and sparse decomposition-based method for seismic random noise attenuation. The proposed method, which is a trace by trace algorithm, starts by transforming the seismic signal into a new sparse subspace using the synchrosqueezing transform. Then, the sparse time-frequency representation (TFR) matrix is decomposed into two parts: (a) a low-rank component and (b) a sp

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25

Liu, Zhoufeng, Baorui Wang, Chunlei Li, Miao Yu, and Shumin Ding. "Fabric defect detection based on deep-feature and low-rank decomposition." Journal of Engineered Fibers and Fabrics 15 (January 2020): 155892502090302. http://dx.doi.org/10.1177/1558925020903026.

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Fabric defect detection plays an important role in controlling the quality of textile production. In this article, a novel fabric defect detection algorithm is proposed based on a multi-scale convolutional neural network and low-rank decomposition model. First, multi-scale convolutional neural network, which can extract the multi-scale deep feature of the image using multiple nonlinear transformations, is adopted to improve the characterization ability of fabric images with complex textures. The effective feature extraction makes the background lie in a low-rank subspace, and a sparse defect d

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26

Zhang Xiaohui, 张晓慧, 郝润芳 Hao Runfang, and 李廷鱼 Li Tingyu. "Hyperspectral Abnormal Target Detection Based on Low Rank and Sparse Matrix Decomposition-Sparse Representation." Laser & Optoelectronics Progress 56, no. 4 (2019): 042801. http://dx.doi.org/10.3788/lop56.042801.

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27

Zhang, Chunjie, Jing Liu, Chao Liang, Zhe Xue, Junbiao Pang, and Qingming Huang. "Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition." Computer Vision and Image Understanding 123 (June 2014): 14–22. http://dx.doi.org/10.1016/j.cviu.2014.02.013.

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28

Wu, Tao, Yu Lei, Jiao Shi, and Maoguo Gong. "An evolutionary multiobjective method for low-rank and sparse matrix decomposition." Big Data & Information Analytics 2, no. 1 (2017): 23–37. http://dx.doi.org/10.3934/bdia.2017006.

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29

Wang, Gang, Qunxi Dong, Jianfeng Wu, et al. "Developing univariate neurodegeneration biomarkers with low-rank and sparse subspace decomposition." Medical Image Analysis 67 (January 2021): 101877. http://dx.doi.org/10.1016/j.media.2020.101877.

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30

Ma, Minsheng, Ruimin Hu, Shihong Chen, Jing Xiao, and Zhongyuan Wang. "Robust background subtraction method via low-rank and structured sparse decomposition." China Communications 15, no. 7 (2018): 156–67. http://dx.doi.org/10.1109/cc.2018.8424611.

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31

Huang, Zhihong, Shutao Li, Leyuan Fang, Huali Li, and Jon Atli Benediktsson. "Hyperspectral Image Denoising With Group Sparse and Low-Rank Tensor Decomposition." IEEE Access 6 (2018): 1380–90. http://dx.doi.org/10.1109/access.2017.2778947.

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32

Zhang, Lihe, and Chen Ma. "Low-rank decomposition and Laplacian group sparse coding for image classification." Neurocomputing 135 (July 2014): 339–47. http://dx.doi.org/10.1016/j.neucom.2013.12.032.

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33

Azghani, Masoumeh, Ashkan Esmaeili, Kayhan Behdin, and Farokh Marvasti. "Missing Low-Rank and Sparse Decomposition Based on Smoothed Nuclear Norm." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 6 (2020): 1550–58. http://dx.doi.org/10.1109/tcsvt.2019.2907467.

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34

Tivive, Fok Hing Chi, Abdesselam Bouzerdoum, and Canicious Abeynayake. "GPR Target Detection by Joint Sparse and Low-Rank Matrix Decomposition." IEEE Transactions on Geoscience and Remote Sensing 57, no. 5 (2019): 2583–95. http://dx.doi.org/10.1109/tgrs.2018.2875102.

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35

Yang, Xi, Xinbo Gao, Dacheng Tao, Xuelong Li, Bing Han, and Jie Li. "Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection." IEEE Transactions on Neural Networks and Learning Systems 27, no. 1 (2016): 32–46. http://dx.doi.org/10.1109/tnnls.2015.2411613.

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36

Chen, Junbo, Shouyin Liu, and Min Huang. "Low-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction." Journal of Healthcare Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9856058.

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The reconstruction of dynamic magnetic resonance imaging (dMRI) from partially sampled k-space data has to deal with a trade-off between the spatial resolution and temporal resolution. In this paper, a low-rank and sparse decomposition model is introduced to resolve this issue, which is formulated as an inverse problem regularized by robust principal component analysis (RPCA). The inverse problem can be solved by convex optimization method. We propose a scalable and fast algorithm based on the inexact augmented Lagrange multipliers (IALM) to carry out the convex optimization. The experimental

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37

Lin, Bo, Jiying Liu, Meihua Xie, and Jubo Zhu. "Direction-of-Arrival Tracking via Low-Rank Plus Sparse Matrix Decomposition." IEEE Antennas and Wireless Propagation Letters 14 (2015): 1302–5. http://dx.doi.org/10.1109/lawp.2015.2403392.

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38

Niu, Shanzhou, Gaohang Yu, Jianhua Ma, and Jing Wang. "Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction." Inverse Problems 34, no. 2 (2018): 024003. http://dx.doi.org/10.1088/1361-6420/aa942c.

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39

Guo, Kailing, Xiaona Xie, Xiangmin Xu, and Xiaofen Xing. "Compressing by Learning in a Low-Rank and Sparse Decomposition Form." IEEE Access 7 (2019): 150823–32. http://dx.doi.org/10.1109/access.2019.2947846.

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40

Li, Wenping, Kun Deng, and Fahong Yu. "Feature-Based Trajectory Privacy Preserving via Low-Rank and Sparse Decomposition." Chinese Journal of Electronics 27, no. 4 (2018): 746–55. http://dx.doi.org/10.1049/cje.2018.04.015.

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41

Ahmed, Junaid, Bin Gao, Wai Lok Woo, and Yuyu Zhu. "Ensemble Joint Sparse Low-Rank Matrix Decomposition for Thermography Diagnosis System." IEEE Transactions on Industrial Electronics 68, no. 3 (2021): 2648–58. http://dx.doi.org/10.1109/tie.2020.2975484.

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42

Gou, Shuiping, Yueyue Wang, Zhilong Wang, et al. "CT Image Sequence Restoration Based on Sparse and Low-Rank Decomposition." PLoS ONE 8, no. 9 (2013): e72696. http://dx.doi.org/10.1371/journal.pone.0072696.

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43

Chong, Jiang. "Moving Target Detection Based on an Adaptive Low-Rank Sparse Decomposition." Computing and Informatics 39, no. 5 (2020): 1061–81. http://dx.doi.org/10.31577/cai_2020_5_1061.

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44

Xiang, Pei, Jiangluqi Song, Huan Li, Lin Gu, and Huixin Zhou. "Hyperspectral Anomaly Detection with Harmonic Analysis and Low-Rank Decomposition." Remote Sensing 11, no. 24 (2019): 3028. http://dx.doi.org/10.3390/rs11243028.

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Hyperspectral anomaly detection methods are often limited by the effects of redundant information and isolated noise. Here, a novel hyperspectral anomaly detection method based on harmonic analysis (HA) and low rank decomposition is proposed. This paper introduces three main innovations: first and foremost, in order to extract low-order harmonic images, a single-pixel-related HA was introduced to reduce dimension and remove redundant information in the original hyperspectral image (HSI). Additionally, adopting the guided filtering (GF) and differential operation, a novel background dictionary

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45

Xue, Zhichao, Jing Dong, Yuxin Zhao, Chang Liu, and Ryad Chellali. "Low-rank and sparse matrix decomposition via the truncated nuclear norm and a sparse regularizer." Visual Computer 35, no. 11 (2018): 1549–66. http://dx.doi.org/10.1007/s00371-018-1555-1.

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46

Zheng, Kai, Yin Bai, Jingfeng Xiong, Feng Tan, Dewei Yang, and Yi Zhang. "Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis." Sensors 20, no. 19 (2020): 5541. http://dx.doi.org/10.3390/s20195541.

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Singular value decomposition (SVD) methods have aroused wide concern to extract the periodic impulses for bearing fault diagnosis. The state-of-the-art SVD methods mainly focus on the low rank property of the Hankel matrix for the fault feature, which cannot achieve satisfied performance when the background noise is strong. Different to the existing low rank-based approaches, we proposed a simultaneously low rank and group sparse decomposition (SLRGSD) method for bearing fault diagnosis. The major contribution is that the simultaneously low rank and group sparse (SLRGS) property of the Hankel

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47

Sun, Yubao, Zhi Li, and Min Wu. "A Rank-Constrained Matrix Representation for Hypergraph-Based Subspace Clustering." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/572753.

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This paper presents a novel, rank-constrained matrix representation combined with hypergraph spectral analysis to enable the recovery of the original subspace structures of corrupted data. Real-world data are frequently corrupted with both sparse error and noise. Our matrix decomposition model separates the low-rank, sparse error, and noise components from the data in order to enhance robustness to the corruption. In order to obtain the desired rank representation of the data within a dictionary, our model directly utilizes rank constraints by restricting the upper bound of the rank range. An

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48

Sun, Chengli, Jianxiao Xie, and Yan Leng. "A Signal Subspace Speech Enhancement Approach Based on Joint Low-Rank and Sparse Matrix Decomposition." Archives of Acoustics 41, no. 2 (2016): 245–54. http://dx.doi.org/10.1515/aoa-2016-0024.

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Abstract Subspace-based methods have been effectively used to estimate enhanced speech from noisy speech samples. In the traditional subspace approaches, a critical step is splitting of two invariant subspaces associated with signal and noise via subspace decomposition, which is often performed by singular-value decomposition or eigenvalue decomposition. However, these decomposition algorithms are highly sensitive to the presence of large corruptions, resulting in a large amount of residual noise within enhanced speech in low signal-to-noise ratio (SNR) situations. In this paper, a joint low-r

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49

Mu, Junsheng, Xiaojun Jing, Hai Huang, and Ning Gao. "A blind spectrum sensing based on low-rank and sparse matrix decomposition." China Communications 15, no. 8 (2018): 118–25. http://dx.doi.org/10.1109/cc.2018.8438278.

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50

Sen, Satyabrata. "Low-Rank Matrix Decomposition and Spatio-Temporal Sparse Recovery for STAP Radar." IEEE Journal of Selected Topics in Signal Processing 9, no. 8 (2015): 1510–23. http://dx.doi.org/10.1109/jstsp.2015.2464187.

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