Locality-constrained weighted collaborative-competitive representation for classification

How to represent and classify a testing sample for the representation-based classification (RBC) plays an important role in the filed of pattern recognition. As a typical kind of the representation-based classification with promising performance, collaborative representation-based classification (CRC) adopts all the training samples to collaboratively represent and then classify each testing sample with the reconstructive residuals among all the classes. However, most of the CRC methods fail to make full use of the localities and discrimination information of data in collaborative representation. To address this issue to further improve the classification performance, we design a novel supervised CRC method entitled locality-constrained weighted collaborative-competitive representation-based classification (LWCCRC). In the proposed method, the localities of data are taken into account by using the positive and negative nearest samples of each testing sample with their corresponding weighted constraints. Such devised locality-constrained weighted term can model the similarity and natural discrimination information contained in the neighborhood region for each testing sample to obtain the favorable representation. Moreover, a competitive constraint is introduced to enhance pattern discrimination among the categorical collaborative representations. To explore the effectiveness of our proposed LWCCRC, the extensive experiments are carried out on three different types of data sets. The experimental results demonstrate that the proposed LWCCRC significantly outperforms the recent state-of-the-art CRC methods.

Automated summary

The representation and classification of testing samples are crucial in pattern recognition. Collaborative representation-based classification (CRC) is a promising approach that utilizes training samples to collaboratively represent and classify testing samples. However, most CRC methods fail to fully exploit the local and discrimination information. To overcome this limitation, a novel supervised CRC method called LWCCRC is proposed, which incorporates local constraints and competition to improve representation. Extensive experiments on different datasets demonstrate that LWCCRC outperforms state-of-the-art CRC methods significantly.