文献类型：期刊文献

英文题名：Dual robust regression for pattern classification

作者：Qian, Jianjun[1,2,3];Zhu, Shumin[1,2];Wong, Wai Keung[3];Zhang, Hengmin[4];Lai, Zhihui[5];Yang, Jian[1,2]

机构：[1]Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Key Lab Intelligent Percept & Syst High Dimens In, Minist Educ,PCA Lab, Nanjing, Peoples R China;[2]Nanjing Univ Sci & Technol, Jiangsu Key Lab Image & Video Understanding Socia, Sch Comp Sci & Engn, Nanjing, Peoples R China;[3]Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Peoples R China;[4]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai, Peoples R China;[5]Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China

年份：2021

卷号：546

起止页码：1014

外文期刊名：INFORMATION SCIENCES

收录：;EI(收录号：20204209348060);WOS:【SCI-EXPANDED(收录号:WOS:000596062600015)】；

基金：This work was supported by the National Science Fund of China under Grant Nos. 61876083, U1713208 and 61906067, in part by the research Grant of Hong Kong Scholars Program and The Hong Kong Polytechnic University (Project Code: YZ2K).

语种：英文

外文关键词：Robust regression; Pattern classification; Low-rank; Sparse representation

摘要：Linear regression-based and extended methods have been widely used in pattern classification. These methods can be roughly divided into two categories: reconstruction error based methods and discriminative methods. The reconstruction error-based methods search the target label by learning the representation coefficients to compute the minimal reconstruction error. The goal of the discriminative methods is to learn the projection matrix to predict the target label of the image. To combine the advantages of these two kinds of regression-based methods, this paper presents a dual robust regression framework (DualRR) for pattern classification. In the training stage, a double low-rank robust regression model (DLR) is proposed to learn the projection matrix. In DLR, low-rank robust regression motivates us to model the data as the sum of a low-rank clean data and sparse noise matrix. The low-rank is further used to constrain the projection matrix to enhance the discriminative performance. In the testing stage, the proposed framework employs a robust regression representation model to learn the optimal representation coefficients and obtain the reconstruction sample to approximate the test sample. We thus apply the reconstruction sample to search the classification label by using the projection matrix learned in the training stage. Extensive experiments are conducted on six public available databases, namely, LFW, FRGC, CUHK Sketch, PolyU Palm, NUST-RF and Caltech 101, demonstrating the merits of the proposed model over state-of-the-art regression-based classification methods. (c) 2020 Elsevier Inc. All rights reserved.