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

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

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[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]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44 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.

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Last Update: 2023-10-22