Welcome to the IKCEST

Computers & Electrical Engineering | Vol.84, Issue. | 2020-05-31 | Pages 106632

Computers & Electrical Engineering

Double competitive constraints-based collaborative representation for pattern classification

Heping Song   Weihua Ou   Shaoning Zeng   Lan Du   Jianping Gou   Hongwei Wu   Jia Ke  
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 be 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 l2-norm regularization of residuals between each query sample and the class-specific representations, another one is the l2-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. The effective and robust classification performance of the proposed method is verified by conducting extensive experiments on six public face databases and twelve real numerical UCI data sets in comparisons with the state-of-the-art CRC methods. The experimental results demonstrate the promising classification performance of the proposed method.

Original Text (This is the original text for your reference.)

Double competitive constraints-based collaborative representation for pattern classification

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 be 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 l2-norm regularization of residuals between each query sample and the class-specific representations, another one is the l2-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. The effective and robust classification performance of the proposed method is verified by conducting extensive experiments on six public face databases and twelve real numerical UCI data sets in comparisons with the state-of-the-art CRC methods. The experimental results demonstrate the promising classification performance of the proposed method.

+More

Cite this article
APA

APA

MLA

Chicago

Heping Song, Weihua Ou, Shaoning Zeng, Lan Du,Jianping Gou, Hongwei Wu, Jia Ke,.Double competitive constraints-based collaborative representation for pattern classification. 84 (),106632.

Disclaimer: The translated content is provided by third-party translation service providers, and IKCEST shall not assume any responsibility for the accuracy and legality of the content.
Translate engine
Article's language
English
中文
Pусск
Français
Español
العربية
Português
Kikongo
Dutch
kiswahili
هَوُسَ
IsiZulu
Action
Recommended articles

Report

Select your report category*



Reason*



By pressing send, your feedback will be used to improve IKCEST. Your privacy will be protected.

Submit
Cancel