Locality-constrained weighted collaborative-competitive representation for classification

Jianping Gou, Xiangshuo Xiong, Hongwei Wu, Lan Du, Shaoning Zeng, Yunhao Yuan, Weihua Ou

Research output: Contribution to journalArticleResearchpeer-review


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.

Original languageEnglish
Pages (from-to)363–376
Number of pages14
JournalInternational Journal of Machine Learning and Cybernetics
Publication statusPublished - Feb 2023


  • Collaborative representation
  • Collaborative representation-based classification
  • Pattern recognition
  • Representation-based classification

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