Double Competitive Constraints-Based Collaborative Representation for Pattern Classification. (June 2020)

• Record Type: Journal Article
• Authors: Gou, Jianping; Wu, Hongwei; Song, Heping; Du, Lan; Ou, Weihua; Zeng, Shaoning; Ke, Jia
• Is Part Of: Computers & electrical engineering. Volume 84(2020)
• Publication Date: 2020-06
• Subjects: Collaborative representation -- Representation-based classification -- Collaborative representation-based classification -- Pattern recognition; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106632
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Representation-based classification (RBC) has attracted much attention in pattern recognition. As a linear representative RBC method, collaborative representation-based classification (CRC) is very promising for classification. Although many extensions of CRC have been developed recently, the discriminative and competitive representations of different classes for favorable classification has not yet been fully explored. To design the discriminative and competitive collaborative representations for enhancing the power of pattern discrimination, we propose a novel double competitive constraints-based collaborative representation for classification (DCCRC). In the proposed DCCRC, one competitive constraint is the l 2 -norm regularization of residuals between each query sample and the class-specific representations, the other one is the l 2 -norm regularization of the representations of all the classes excluding any one class. In two competitive constraints, the class discrimination information is employed to generate competitive representations. Moreover, the proposed method integrates both the representation learning and classification into the unified model. We study the effectiveness and robustness of the proposed method by comparing it with the state-of-the-art CRC methods on six face databases and twelve UCI data sets. The experimental results demonstrate the promising classification performance of the proposed method.