Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (2): 191-197.doi: 10.11896/jsjkx.210300034

• Database & Big Data & Data Science • Previous Articles     Next Articles

Robust Joint Sparse Uncorrelated Regression

LI Zong-ran1, CHEN XIU-Hong1,2, LU Yun1, SHAO Zheng-yi1   

  1. 1 School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi,Jiangsu 214122,China
    2 Jiangsu Key Laboratory of Media Design and Software Technology,Wuxi,Jiangsu 214122,China
  • Received:2021-03-02 Revised:2021-07-10 Online:2022-02-15 Published:2022-02-23
  • About author:LI Zong-Ran,born in 1995,postgra-duate.His main research interests include machine learning,pattern recognition and digital image processing.
    CHEN Xiu-hong,born in 1964,Ph.D.His main research interests include di-gital image processing,pattern recognition,target detection and tracking,optimization theory and method and so on.

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Abstract: Common unsupervised feature selection methods only consider the selection of discriminative features,while ignoring the redundancy of features and failing to consider the problem of small classes,which affect the classification performance.Based on this background,a robust uncorrelated regression algorithm is proposed.First,research on uncorrelated regression,use uncorrelated orthogonal constraints to find irrelevant but discriminative features.Uncorrelated constraints keep the data structure in the Stiefel manifold,making the model have a closed solution,avoiding the possible trivial solutions caused by the traditional ridge regression model.Secondly,the loss function and the regularization term use the L2,1 norm to ensure the robustness of the model and obtain a sparse projection matrix.At the same time,the small class problem is taken into account,so that the number of projection matrices is not limited by the number of classes,and the result is enough projection matrices to improve the classification performance of the model.Theoretical analysis and experimental results on multiple data sets show that the proposed method has better performance than other feature selection methods.

Key words: Feature selection, Joint, Regression, Robust, Small-class, Uncorrelated

CLC Number: 

  • TP391.4
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