Sparse, collaborative, or nonnegative representation: Which helps pattern classification?

Jun Xu, Wangpeng An, Lei Zhang, David Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

96 Citations (Scopus)


The use of sparse representation (SR) and collaborative representation (CR) for pattern classification has been widely studied in tasks such as face recognition and object categorization. Despite the success of SR/CR based classifiers, it is still arguable whether it is the ℓ1-norm sparsity or the ℓ2-norm collaborative property that brings the success of SR/CR based classification. In this paper, we investigate the use of nonnegative representation (NR) for pattern classification, which is largely ignored by previous work. Our analyses reveal that NR can boost the representation power of homogeneous samples while limiting the representation power of heterogeneous samples, making the representation sparse and discriminative simultaneously and thus providing a more effective solution to representation based classification than SR/CR. Our experiments demonstrate that the proposed NR based classifier (NRC) outperforms previous representation based classifiers. With deep features as inputs, it also achieves state-of-the-art performance on various visual classification tasks.

Original languageEnglish
Pages (from-to)679-688
Number of pages10
JournalPattern Recognition
Publication statusPublished - 1 Apr 2019


  • Collaborative representation
  • Nonnegative representation
  • Pattern classification
  • Sparse representation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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