Low-rank Sparse Representation Based on Elastic Least Squares Regression Learning

NAVIGATE

Table of Contents

STATISTICS

Viewed930

Downloads828

Low-rank Sparse Representation Based on Elastic Least Squares Regression Learning

[HTML] PDF下载 (828)

[1]WU Jigang,LI Miaojun,ZHAO Shuping.Low-rank Sparse Representation Based on Elastic Least Squares Regression Learning[J].Journal of Zhengzhou University (Engineering Science),2023,44(06):25-32.[doi:10.13705/j.issn.1671-6833.2023.03.011]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 06 Page number: 25-32 Column: Public date: 2023-12-25

Title:: Low-rank Sparse Representation Based on Elastic Least Squares Regression Learning

Author(s):: WU Jigang; LI Miaojun; ZHAO Shuping; 1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, China

Keywords:: sparse representation; least squares regression; low-rank representation; flexibility; sparse error term

CLC:: TP181

DOI:: 10.13705/j.issn.1671-6833.2023.03.011

Abstract:: In order to overcome the disadvantage that the redirected least squares regression model might destroy the structure of the regression target, a low-rank sparse representation based elastic least squares regression learning ( LRSR-eLSR)model was proposed. The model based on the least squares regression, but did not use the strict 0-1 label matrix as the target matrix. Instead, it introduced the margin constraint to directly learn the regression objective from the data, which could increase the flexibility of the regression model while maintaining the regression target structure. Moreover, in order to capture the structure information of the data, a low rank representation of the data was used to maintain the structure of the data. In the process of calculation, considering the complexity of the problem, the kernel norm regularization was used instead of the rank function. In addition to this, the model introduced a sparse error term with a L2,1 -norm to compensate for regression errors, which facilitates learning more flexible transformations. The model also imposed additional regularization terms on the projection matrix to avoid overfitting. The experimental results showed that the recognition accuracy of the model in this paper is better than that of other methods on four published datasets. The recognition rate could be up to 98% in the COIL-20 dataset.

References:: [1] MA J J, ZHOU S S. Discriminative least squares regression for multiclass classification based on within-class scatter minimization[ J] . Applied Intelligence, 2022, 52 (1) : 622-635.
[2] 蔡雨虹. 鉴别性稀疏与低秩表示的算法研究[ D] . 无锡: 江南大学, 2021.
CAI Y H. Algorithm research on discriminative sparse and low-rank representation [ D] . Wuxi: Jiangnan University, 2021.
[3] WEN J, XU Y, LI Z Y, et al. Inter-class sparsity based discriminative least square regression [ J ] . Neural Networks, 2018, 102: 36-47.
[4] ZHANG X Y, WANG L F, XIANG S M, et al. Retargeted least squares regression algorithm[ J] . IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(9) : 2206-2213.
[5] ZHANG Z, LAI Z H, XU Y, et al. Discriminative elastic-net regularized linear regression [ J] . IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 2017, 26(3) : 1466-1481.
[6] ZHAO S P, WU J G, ZHANG B, et al. Low-rank interclass sparsity based semi-flexible target least squares regression for feature representation [ J] . Pattern Recognition, 2022, 123: 108346.
[7] 朱文生, 何显文. 结合加权低秩表示和 L1 范数的图像混合去噪[ J] . 赣南师范大学学报, 2022, 43( 3) : 116-120.
ZHU W S, HE X W. Image hybrid denoising via weighted low-rank representation and L1 -norm [ J] . Journal of Gannan Normal University, 2022, 43(3) : 116-120.
[8] PENG Y L, ZHANG L, LIU S G, et al. Kernel negative ε dragging linear regression for pattern classification[ J] . Complexity, 2017, 2017: 1-14.
[9] 赵雯, 吴小俊. 基于鉴别性低秩表示及字典学习的鲁棒人脸识别算法 [ J ] . 计算机应用研究, 2017, 34 (10) : 3157-3161.
ZHAO W, WU X J. Robust face recognition of discriminative low-rank representation with dictionary learning [ J ] . Application Research of Computers, 2017, 34 (10) : 3157-3161.
[10] BAO J Q, LAI Z H, LI X C. Relaxed local preserving regression for image feature extraction[ J] . Multimedia Tools and Applications, 2021, 80(3) : 3729-3748.
[11] FANG X Z, TENG S H, LAI Z H, et al. Robust latent subspace learning for image classification [ J ] . IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(6) : 2502-2515.
[12] 杨章静, 王文博, 黄璞, 等. 基于潜子空间去噪的子空间学习图像分类方法 [ J] . 计算机科学与探索, 2021, 15(12) : 2374-2389.
YANG Z J, WANG W B, HUANG P, et al. Denoising latent subspace based subspace learning for image classification[ J] . Journal of Frontiers of Computer Science and Technology, 2021, 15(12) : 2374-2389.
[13] 钟堃琰,刘惊雷. 基于低秩类间稀疏判别最小二乘回归的图像分类[ J] . 山东大学学报(理学版) , 2022, 57 (7) :1-13.
ZHONG K Y, LIU J L. Image classification based on low-rank inter-class sparsity discriminant least squares regression[ J] . Journal of Shandong University(Natural Science) , 2022, 57(7) :1-13.
[14] CHEN Z, WU X J, KITTLER J. Fisher discriminative least squares regression for image classification [ EB / OL] . ( 2019 - 07 - 11) [ 2022 - 10 - 05] . https:∥arxiv. org / abs/ 1903. 07833.
[15] FANG X Z, HAN N, WU J G, et al. Approximate lowrank projection learning for feature extraction[ J] . IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(11) : 5228-5241.
[16] 王杰, 李胜光, 宋一帆, 等. 图像去模糊的自适应交替方向乘子重叠组稀疏方法[ J] . 郑州大学学报( 工学版) , 2018, 39(5) : 52-57, 78.
WANG J, LI S G, SONG Y F, et al. Image deblurring using adaptive alternate direction multiplier overlapping group sparsity method[ J] . Journal of Zhengzhou University (Engineering Science) , 2018, 39(5) : 52-57, 78.
[17] CHEN Z, WU X J, KITTLER J. Low-rank discriminative least squares regression for image classification[ J] . Signal Processing, 2020, 173: 107485.
[18] MENG M, LAN M C, YU J, et al. Constrained discriminative projection learning for image classification [ J ]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 2020, 29: 186-198. [19] CHEN Z, WU X J, CAI Y H, et al. Sparse non-negative transition subspace learning for image classification [ J] . Signal Processing, 2021, 183: 107988.
[20] 姚欣, 邢砾云, 辛平. 基于小波特征提取与深度学习的微电网故障诊断与分类方法[ J] . 智慧电力, 2021, 49(12) : 17-24.
YAO X, XING L Y, XIN P. Fault diagnosis and classification of microgrid based on wavelet feature extraction and deep learning[J]. Smart Power, 2021, 49(12): 17-24.

Similar References:

Memo

Last Update: 2023-10-22