Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (10): 185-190.doi: 10.11896/jsjkx.200800219

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

Sample Feature Kernel Matrix-based Sparse Bilinear Regression

SHAO Zheng-yi1, CHEN Xiu-hong1,2   

  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:2020-08-30 Revised:2020-11-11 Online:2021-10-15 Published:2021-10-18
  • About author:SHAO Zheng-yi,born in 1996,M.S candidate.His main research interests include pattern recognition,linear regression,etc.

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Abstract: There are a large number of redundant data in many real applications,which may be high dimensional.In this case,there will be many problems in regression prediction,such as overfitting and low prediction accuracy.In addition,most regression methods are based on vectors,ignoring the relationship between the original positions of matrix data.To this end,a sample kernel matrix-based sparse bilinear regression (KMSBR) method is proposed.The KMSBR model which use the sample feature kernel matrix and L2,1-norm is established through the left and right regression coefficient matrix.Thus,the KMSBR can implement the selection of samples and its features simultaneously.Experimental results on several data sets show that KMSBR can effectively select samples and its features,thus improve the efficiency of the algorithm,and the prediction accuracy is better than the existing regression models.

Key words: Feature kernel matrix, Left and right regression matrix, Linear regression, Sample and feature extraction, Sparsity

CLC Number: 

  • TP391.4
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