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Journal articles on the topic 'Joint-sparse'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Zhang, Dong, Yongshun Zhang, and Cunqian Feng. "Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction." International Journal of Antennas and Propagation 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/6862852.

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Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational effici

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2

Jianyuan Li, and Jihong Guan. "Learning with Joint Sparse Representation." International Journal of Advancements in Computing Technology 4, no. 6 (2012): 184–91. http://dx.doi.org/10.4156/ijact.vol4.issue6.22.

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3

Liu, Haibiao, Zhihui Lai, and Yudong Chen. "Joint Sparse Neighborhood Preserving Embedding." Journal of Physics: Conference Series 1176 (March 2019): 032023. http://dx.doi.org/10.1088/1742-6596/1176/3/032023.

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4

Yi, Shuangyan, Zhihui Lai, Zhenyu He, Yiu-ming Cheung, and Yang Liu. "Joint sparse principal component analysis." Pattern Recognition 61 (January 2017): 524–36. http://dx.doi.org/10.1016/j.patcog.2016.08.025.

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5

Khanna, Saurabh, and Chandra R. Murthy. "Decentralized Joint-Sparse Signal Recovery: A Sparse Bayesian Learning Approach." IEEE Transactions on Signal and Information Processing over Networks 3, no. 1 (2017): 29–45. http://dx.doi.org/10.1109/tsipn.2016.2612120.

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6

Khanna, Saurabh, and Chandra R. Murthy. "Communication-Efficient Decentralized Sparse Bayesian Learning of Joint Sparse Signals." IEEE Transactions on Signal and Information Processing over Networks 3, no. 3 (2017): 617–30. http://dx.doi.org/10.1109/tsipn.2016.2632041.

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7

Sima, Haifeng, Pei Liu, Lanlan Liu, Aizhong Mi, and Jianfang Wang. "Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images." Mathematical Problems in Engineering 2018 (October 2, 2018): 1–10. http://dx.doi.org/10.1155/2018/8264961.

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Aiming at solving the difficulty of modeling on spatial coherence, complete feature extraction, and sparse representation in hyperspectral image classification, a joint sparse representation classification method is investigated by flexible patches sampling of superpixels. First, the principal component analysis and total variation diffusion are employed to form the pseudo color image for simplifying superpixels computing with (simple linear iterative clustering) SLIC model. Then, we design a joint sparse recovery model by sampling overcomplete patches of superpixels to estimate joint sparse c

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8

Tosic, Ivana, and Sarah Drewes. "Learning Joint Intensity-Depth Sparse Representations." IEEE Transactions on Image Processing 23, no. 5 (2014): 2122–32. http://dx.doi.org/10.1109/tip.2014.2312645.

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9

Davies, Mike E., and Yonina C. Eldar. "Rank Awareness in Joint Sparse Recovery." IEEE Transactions on Information Theory 58, no. 2 (2012): 1135–46. http://dx.doi.org/10.1109/tit.2011.2173722.

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10

Lee, Kiryung, Yoram Bresler, and Marius Junge. "Subspace Methods for Joint Sparse Recovery." IEEE Transactions on Information Theory 58, no. 6 (2012): 3613–41. http://dx.doi.org/10.1109/tit.2012.2189196.

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11

Wen, Zaidao, Biao Hou, and Licheng Jiao. "Joint Sparse Recovery With Semisupervised MUSIC." IEEE Signal Processing Letters 24, no. 5 (2017): 629–33. http://dx.doi.org/10.1109/lsp.2017.2680603.

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12

Miao, Jianyu, Heling Cao, Xiao-Bo Jin, Rongrong Ma, Xuan Fei, and Lingfeng Niu. "Joint Sparse Regularization for Dictionary Learning." Cognitive Computation 11, no. 5 (2019): 697–710. http://dx.doi.org/10.1007/s12559-019-09650-2.

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13

Blanchard, Jeffrey D., Michael Cermak, David Hanle, and Yirong Jing. "Greedy Algorithms for Joint Sparse Recovery." IEEE Transactions on Signal Processing 62, no. 7 (2014): 1694–704. http://dx.doi.org/10.1109/tsp.2014.2301980.

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14

Adcock, Ben, Anne Gelb, Guohui Song, and Yi Sui. "Joint Sparse Recovery Based on Variances." SIAM Journal on Scientific Computing 41, no. 1 (2019): A246—A268. http://dx.doi.org/10.1137/17m1155983.

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15

Wei, Wang, Tang Can, Wang Xin, Luo Yanhong, Hu Yongle, and Li Ji. "Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation." Computational Intelligence and Neuroscience 2019 (November 21, 2019): 1–9. http://dx.doi.org/10.1155/2019/8258275.

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An image object recognition approach based on deep features and adaptive weighted joint sparse representation (D-AJSR) is proposed in this paper. D-AJSR is a data-lightweight classification framework, which can classify and recognize objects well with few training samples. In D-AJSR, the convolutional neural network (CNN) is used to extract the deep features of the training samples and test samples. Then, we use the adaptive weighted joint sparse representation to identify the objects, in which the eigenvectors are reconstructed by calculating the contribution weights of each eigenvector. Aimi

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16

Sunil Kumar, M. ,., C. K. ,. Narayanappa, and M. Nagendra Kumar. "Optimization of Sparse Learning Problem of Signals on Hybrid mm-Wave MIMO Systems using Sparse Coding based Reconstruction Learning Mechanism." International Journal of Circuits, Systems and Signal Processing 15 (July 22, 2021): 713–21. http://dx.doi.org/10.46300/9106.2021.15.79.

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Researchers and industry experts are looking for the availability of large bandwidth spectrum due to high market demands and expectations for high data rates. And Millimeter Wave technology possess characteristics to fulfill these requirements. However, due to high power consumption and channel estimation requirements, massive MIMO is utilized in coordination with Millimeter Wave technology. Besides, the performance of mm-WAVE MIMO system is measured by the effective estimation of Channel State Information (CSI) which is a critical and challenging process. Therefore, a Sparse Coding based Reco

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17

Lai, Ke, Lei Wen, Jing Lei, Pei Xiao, Amine Maaref, and Muhammad Ali Imran. "Sub-Graph Based Joint Sparse Graph for Sparse Code Multiple Access Systems." IEEE Access 6 (2018): 25066–80. http://dx.doi.org/10.1109/access.2018.2828126.

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18

Wang, Jun, Fenggang Yan, Yinan Zhao, and Xiaolin Qiao. "Sparse Scenario Imaging for Active Radar in the Forward-Looking Direction." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/653208.

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The resolution of multiple targets at the same range cell but different angles in the forward-looking direction is of great trouble for active radar. Based on compressive sensing (CS) framework, a sparse scenario imaging approach using joint angle-Doppler representation basis is proposed, which employs multisensor and single-receiver channel hardware architecture. Firstly, the joint angle-Doppler representation basis is formulated using the Doppler dictionary, and then the radar returns during multiple pulse repetition periods are modeled as the measurements with respect to a stationary sparse

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19

Zarezade, Ali, Hamid R. Rabiee, Ali Soltani-Farani, and Ahmad Khajenezhad. "Patchwise Joint Sparse Tracking With Occlusion Detection." IEEE Transactions on Image Processing 23, no. 10 (2014): 4496–510. http://dx.doi.org/10.1109/tip.2014.2346029.

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20

Wen, Lei, Pei Xiao, Razieh Razavi, et al. "Joint Sparse Graph for FBMC/OQAM Systems." IEEE Transactions on Vehicular Technology 67, no. 7 (2018): 6098–112. http://dx.doi.org/10.1109/tvt.2018.2810638.

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21

Dong, Wenhui, Faliang Chang, and Zijian Zhao. "Visual tracking with multifeature joint sparse representation." Journal of Electronic Imaging 24, no. 1 (2015): 013006. http://dx.doi.org/10.1117/1.jei.24.1.013006.

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22

Yao, Yao, Ping Guo, Xin Xin, and Ziheng Jiang. "Image Fusion by Hierarchical Joint Sparse Representation." Cognitive Computation 6, no. 3 (2013): 281–92. http://dx.doi.org/10.1007/s12559-013-9235-y.

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23

Xiao-Tong Yuan, Xiaobai Liu, and Shuicheng Yan. "Visual Classification With Multitask Joint Sparse Representation." IEEE Transactions on Image Processing 21, no. 10 (2012): 4349–60. http://dx.doi.org/10.1109/tip.2012.2205006.

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24

Hyder, M. M., and K. Mahata. "A Robust Algorithm for Joint-Sparse Recovery." IEEE Signal Processing Letters 16, no. 12 (2009): 1091–94. http://dx.doi.org/10.1109/lsp.2009.2028107.

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25

Kim, Kyung-Su, and Sae-Young Chung. "Greedy subspace pursuit for joint sparse recovery." Journal of Computational and Applied Mathematics 352 (May 2019): 308–27. http://dx.doi.org/10.1016/j.cam.2018.11.027.

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26

Wu, Jianning, Jiajing Wang, and Yun Ling. "DCS-based MBSBL joint reconstruction of multi-sensors data for energy-efficient telemonitoring of human activity." International Journal of Distributed Sensor Networks 14, no. 3 (2018): 155014771876761. http://dx.doi.org/10.1177/1550147718767612.

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The joint reconstruction of nonsparse multi-sensors data with high quality is a challenging issue in human activity telemonitoring. In this study, we proposed a novel joint reconstruction algorithm combining distributed compressed sensing with multiple block sparse Bayesian learning. Its basic idea is that based on the joint sparsity model, the distributed compressed sensing technique is first applied to simultaneously compress the multi-sensors data for gaining the high-correlation information regarding activity as well as the energy efficiency of sensors, and then, the multiple block sparse

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27

Luo, Xiaozhuo, F. Liu, Shuyuan Yang, Xiaodong Wang, and Zhiguo Zhou. "Joint sparse regularization based Sparse Semi-Supervised Extreme Learning Machine (S3ELM) for classification." Knowledge-Based Systems 73 (January 2015): 149–60. http://dx.doi.org/10.1016/j.knosys.2014.09.014.

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28

Fan, Qimeng, Chengyou Yin, and Han Liu. "Accurate Recovery of Sparse Objects With Perfect Mask Based on Joint Sparse Reconstruction." IEEE Access 7 (2019): 73504–15. http://dx.doi.org/10.1109/access.2019.2919962.

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29

Chen, Tao, Huanxin Wu, and Lutao Liu. "A Joint Doppler Frequency Shift and DOA Estimation Algorithm Based on Sparse Representations for Colocated TDM-MIMO Radar." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/421391.

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We address the problem of a new joint Doppler frequency shift (DFS) and direction of arrival (DOA) estimation for colocated TDM-MIMO radar that is a novel technology applied to autocruise and safety driving system in recent years. The signal model of colocated TDM-MIMO radar with few transmitter or receiver channels is depicted and “time varying steering vector” model is proved. Inspired by sparse representations theory, we present a new processing scheme for joint DFS and DOA estimation based on the new input signal model of colocated TDM-MIMO radar. An ultracomplete redundancy dictionary for

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30

Qi, Kai, Jingwen Tu, and Hu Yang. "Joint sparse principal component regression with robust property." Expert Systems with Applications 187 (January 2022): 115845. http://dx.doi.org/10.1016/j.eswa.2021.115845.

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31

Rizkinia, Mia, and Masahiro Okuda. "Joint Local Abundance Sparse Unmixing for Hyperspectral Images." Remote Sensing 9, no. 12 (2017): 1224. http://dx.doi.org/10.3390/rs9121224.

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32

Khan, Zohaib, Faisal Shafait, and Ajmal Mian. "Joint Group Sparse PCA for Compressed Hyperspectral Imaging." IEEE Transactions on Image Processing 24, no. 12 (2015): 4934–42. http://dx.doi.org/10.1109/tip.2015.2472280.

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33

Peng, Jie, Pei Wang, Nengfeng Zhou, and Ji Zhu. "Partial Correlation Estimation by Joint Sparse Regression Models." Journal of the American Statistical Association 104, no. 486 (2009): 735–46. http://dx.doi.org/10.1198/jasa.2009.0126.

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34

Wen, Lei, Razieh Razavi, Muhammad Ali Imran, and Pei Xiao. "Design of Joint Sparse Graph for OFDM System." IEEE Transactions on Wireless Communications 14, no. 4 (2015): 1823–36. http://dx.doi.org/10.1109/twc.2014.2373379.

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35

Kumar, Krishna, Sankha Subhra Bhattacharjee, and Nithin V. George. "Joint Logarithmic Hyperbolic Cosine Robust Sparse Adaptive Algorithms." IEEE Transactions on Circuits and Systems II: Express Briefs 68, no. 1 (2021): 526–30. http://dx.doi.org/10.1109/tcsii.2020.3007798.

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36

Shekhar, Sumit, Vishal M. Patel, Nasser M. Nasrabadi, and Rama Chellappa. "Joint Sparse Representation for Robust Multimodal Biometrics Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 1 (2014): 113–26. http://dx.doi.org/10.1109/tpami.2013.109.

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37

Kim, Junhan, Jian Wang, Luong Trung Nguyen, and Byonghyo Shim. "Joint Sparse Recovery Using Signal Space Matching Pursuit." IEEE Transactions on Information Theory 66, no. 8 (2020): 5072–96. http://dx.doi.org/10.1109/tit.2020.2986917.

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38

Sun, Ying. "Hand posture recognition via joint feature sparse representation." Optical Engineering 50, no. 12 (2011): 127210. http://dx.doi.org/10.1117/1.3662884.

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39

Hou, Jinyong, Changlong Wang, Zixuan Zhao, Feng Zhou, and Huaji Zhou. "A New Method for Joint Sparse DOA Estimation." Sensors 24, no. 22 (2024): 7216. http://dx.doi.org/10.3390/s24227216.

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To tackle the issue of poor accuracy in single-snapshot data processing for Direction of Arrival (DOA) estimation in passive radar systems, this paper introduces a method for judiciously leveraging multi-snapshot data. This approach effectively enhances the accuracy of DOA estimation and spatial angle resolution in passive radar systems. Additionally, in response to the non-convex nature of the mixed norm, we propose a hyperbolic tangent model as a replacement, transforming the problem into a directly solvable convex optimization problem. The rationality of this substitution is thoroughly demo

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40

Xinpeng Du, Lizhi Cheng, and Lufeng Liu. "A Swarm Intelligence Algorithm for Joint Sparse Recovery." IEEE Signal Processing Letters 20, no. 6 (2013): 611–14. http://dx.doi.org/10.1109/lsp.2013.2260822.

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41

Yang, Zai, and Lihua Xie. "Exact Joint Sparse Frequency Recovery via Optimization Methods." IEEE Transactions on Signal Processing 64, no. 19 (2016): 5145–57. http://dx.doi.org/10.1109/tsp.2016.2576422.

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42

Cui, Zhen, Hong Chang, Shiguang Shan, Bingpeng Ma, and Xilin Chen. "Joint sparse representation for video-based face recognition." Neurocomputing 135 (July 2014): 306–12. http://dx.doi.org/10.1016/j.neucom.2013.12.004.

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43

Esposito, Flavia, Nicolas Gillis, and Nicoletta Del Buono. "Orthogonal joint sparse NMF for microarray data analysis." Journal of Mathematical Biology 79, no. 1 (2019): 223–47. http://dx.doi.org/10.1007/s00285-019-01355-2.

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44

HEUBERGER, CLEMENS, and SARA KROPF. "Analysis of the Binary Asymmetric Joint Sparse Form." Combinatorics, Probability and Computing 23, no. 6 (2014): 1087–113. http://dx.doi.org/10.1017/s0963548314000352.

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We consider redundant binary joint digital expansions of integer vectors. The redundancy is used to minimize the Hamming weight,i.e., the number of non-zero digit vectors. This leads to efficient linear combination algorithms in abelian groups, which are used in elliptic curve cryptography, for instance.If the digit set is a set of contiguous integers containing zero, a special syntactical condition is known to minimize the weight. We analyse the optimal weight of all non-negative integer vectors with maximum entry less thanN. The expectation and the variance are given with a main term and a p

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45

Li, Xiaobao. "Multishot person reidentification using joint group sparse representation." Journal of Electronic Imaging 27, no. 06 (2018): 1. http://dx.doi.org/10.1117/1.jei.27.6.063012.

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46

Mo, Dongmei, Zhihui Lai, and Waikeung Wong. "Locally Joint Sparse Marginal Embedding for Feature Extraction." IEEE Transactions on Multimedia 21, no. 12 (2019): 3038–52. http://dx.doi.org/10.1109/tmm.2019.2916093.

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47

Liu, Chanzi, Qingchun Chen, Bingpeng Zhou, and Hengchao Li. "l1- andl2-Norm Joint Regularization Based Sparse Signal Reconstruction Scheme." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3567095.

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Many problems in signal processing and statistical inference involve finding sparse solution to some underdetermined linear system of equations. This is also the application condition of compressive sensing (CS) which can find the sparse solution from the measurements far less than the original signal. In this paper, we proposel1- andl2-norm joint regularization based reconstruction framework to approach the originall0-norm based sparseness-inducing constrained sparse signal reconstruction problem. Firstly, it is shown that, by employing the simple conjugate gradient algorithm, the new formula

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48

Zang, Miao, Huimin Xu, and Yongmei Zhang. "Kernel-Based Multiview Joint Sparse Coding for Image Annotation." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/6727105.

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It remains a challenging task for automatic image annotation problem due to the semantic gap between visual features and semantic concepts. To reduce the gap, this paper puts forward a kernel-based multiview joint sparse coding (KMVJSC) framework for image annotation. In KMVJSC, different visual features as well as label information are considered as distinct views and are mapped to an implicit kernel space, in which the original nonlinear separable data become linearly separable. Then, all the views are integrated into a multiview joint sparse coding framework aiming to find a set of optimal

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49

Han, Li, Bing Yu, Jingyu Piao, Yuning Tong, Pengyan Lan, and Shuning Liu. "Multi-channel Joint Sparse Learning Model for Non-rigid Three-dimensional Object Classification." Journal of Imaging Science and Technology 64, no. 3 (2020): 30503–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.3.030503.

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Abstract In order to solve the issues of inadequate feature description and inefficient feature learning model existing in current classification methods, this article proposes a multi-channel joint sparse learning model for three-dimensional (3D) non-rigid object classification. First, the authors adopt a multi-level measurement of intrinsic properties to create complementary shape descriptors. Second, they build independent and informative bag of features (BoF) by embedding these shape descriptors into the visual vocabulary space. Third, a max-dependency and min-redundancy criterion is appli

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50

Zhou, Caiyue, Yanfen Kong, Chuanyong Zhang, Lin Sun, Dongmei Wu, and Chongbo Zhou. "A Hybrid Sparse Representation Model for Image Restoration." Sensors 22, no. 2 (2022): 537. http://dx.doi.org/10.3390/s22020537.

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Group-based sparse representation (GSR) uses image nonlocal self-similarity (NSS) prior to grouping similar image patches, and then performs sparse representation. However, the traditional GSR model restores the image by training degraded images, which leads to the inevitable over-fitting of the data in the training model, resulting in poor image restoration results. In this paper, we propose a new hybrid sparse representation model (HSR) for image restoration. The proposed HSR model is improved in two aspects. On the one hand, the proposed HSR model exploits the NSS priors of both degraded im

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