Low-rank constrained weighted discriminative regression for multi-view feature learning  

作　　者：Chao Zhang Huaxiong Li 

机构地区：[1]Department of Control and Systems Engineering,Nanjing University,Nanjing,210093,China

出　　处：《CAAI Transactions on Intelligence Technology》2021年第4期471-479,共9页智能技术学报（英文）

基　　金：National Nature Science Foundation of China,Gtant/Award Numbers:71671086,71732003;The authors would like to thank the editor and anonymous reviewers for their constructive and valuable comments and suggestions.This work was partially supported by National Key Research and Development Program of China(No.2018YFB1402600,2016YFD0702100)。

摘　　要：In recent years,multi-view learning has attracted much attention in the fields of data mining,knowledge discovery and machine learning,and been widely used in classification,clustering and information retrieval,and so forth.A new supervised feature learning method for multi-view data,called low-rank constrained weighted discriminative regression(LWDR),is proposed.Different from previous methods handling each view separately,LWDR learns a discriminative projection matrix by fully exploiting the complementary information among all views from a unified perspective.Based on least squares regression model,the highdimensional multi-view data is mapped into a common subspace,in which different views have different weights in ptojection.The weights are adaptively updated to estimate the roles of all views.To improve the intra-class similarity of learned features,a low-rank constraint is designed and imposed on the multi-view features of each class,which improves the feature discrimination.An iterative optimization algorithm is designed to solve the LWDR model efficiently;Experiments on four popular datasets,including Handwritten,CaltechlOl,PIE and AwA,demonstrate the effectiveness of the proposed method.

关 键 词：method CONSTRAINED WEIGHTS 

分 类 号：O17[理学—数学]