Abstract
Recently, collaborative representation‐based classification (CRC) and its many variations have been widely applied for various classification tasks in pattern recognition. To further enhance the pattern discrimination of CRC, in this article we propose a novel extension of CRC, entitled discriminative, competitive, and collaborative representation‐based classification (DCCRC). In the proposed DCCRC, the class discrimination information is fully utilized for promoting the true class of each testing sample to dominantly represent the testing sample during collaborative representation. The class discrimination information is well considered in the newly designed discriminative l2‐norm regularization that can decrease the ability of representation from the interclasses of each testing sample. Simultaneously, a competitive l2‐norm regularization is introduced to the DCCRC model with the class discrimination information with the aim of enhancing the competitive ability of representation from the true class of each testing sample. The effectiveness of the proposed DCCRC is explored by extensive experiments on the several public face databases and some real numerical UCI data sets. The experimental results demonstrate that the proposed DCCRC achieves the superior performance over the state‐of‐the‐art representation‐based classification methods.
Highlights
Nowadays, the linear representation-based classi cation (RBC) often including sparse representation-based classication (SRC) [1] and collaborative representation-based classi cation (CRC) [2] has attracted more and more attention in pattern recognition
Based on the fact that the discrimination information of data can be explored for enhancing the power of pattern discrimination in collaborative representation, in this article we proposed a novel discriminative competitive and collaborative representation-based classification method (DCCRC) by using the discriminative representation among all the classes. e proposed DCCRC assumes that each class can discriminatively and competitively represent the testing samples. e discriminative and competitive collaborative representations among all the classes can be realized by two l2-norm regularizations in the DCCRC model
P− i)− 1XTy, the class-specific representation residuals are calculated as ‖XiSi − y‖22 and the testing sample y is classified into the class with the minimum representation residual among all the classes
Summary
The linear representation-based classi cation (RBC) often including sparse representation-based classication (SRC) [1] and collaborative representation-based classi cation (CRC) [2] has attracted more and more attention in pattern recognition. With the aim of obtaining the similar competitive representations among all the classes, the discriminative l2-norm regularization of the representations of all the classes except any one class was designed for proposing the competitive and collaborative representation classification method (Co-CRC) [47]. Based on the fact that the discrimination information of data can be explored for enhancing the power of pattern discrimination in collaborative representation, in this article we proposed a novel discriminative competitive and collaborative representation-based classification method (DCCRC) by using the discriminative representation among all the classes. (1) A new discriminative l2-norm regularization is designed by using the representations from all the classes excluding any one class (2) A novel discriminative, competitive, and collaborative representation is proposed for classification by considering the discrimination information of data (3) e experimental analyses are reported for well demonstrating the effectiveness of the proposed DCCRC e rest of this article is organized as follows.
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