Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 151-156.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Novel Method of Improved Low Rank Linear Regression

YU Chuan-bo,NIE Ren-can,ZHOU Dong-ming, HUANG Fan, DING Ting-ting   

  1. School of Information Science and Engineering,Yunnan University,Kunming 650500,China
  • Online:2018-06-20 Published:2018-08-03

Abstract: The low rank linear regression model has good robustness for the influence of occlusion and illumination and so on.To a certain extent,the overfitting phenomenon of LRLR (Low Rank Linear Regression) is reduced in LRRR (Low Rank Ridge Regression) and DENLR (Discriminative Elastic-Net Regularized Linear Regression) by regularization coefficient matrix.Because the error approximation of data in subspace is ill-considered,the data are hardly mapped to the target space accurately via projection matrix.This paper proposed has low rank linear regression classification method which has a faster computing speed and is more discriminative.Firstly,the 0-1 constitutive matrix is regarded as the target value of the linear regression.Secondly,the kernel norm is used as the convex approximation of low rank constraints.Thirdly,all kinds of the distance matrix and the model output matrix are regularized to reduce overfitting phenomenon,at the same time it can enhance the spatial discriminant of projection subspace.Then,the augmented Lagrange multiplier (ALM) is used to optimize the objective function.Finally,the nearest neighbor classifier is used for classification in subspace.We compared the related algorithms on AR,FERET face database,Stanford 40 Actions database,Caltech-UCSD Birds database and Oxford 102 Flowers database.The experimental results show that the proposed algorithm is effective.

Key words: Classification, Linear regression, Low rank, Regularization

CLC Number: 

  • TP391.41
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