Discriminative Collaborative Representation and Its Application to Audio Signal Classification

Yuechi Jiang, Frank H.F. Leung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose Discriminative Collaborative Representation (DCR) as an extension to Collaborative Representation (CR), by adding an extra discriminative term to the original formulation of CR. In the literature, both CR and Sparse Representation (SR) have been shown to be good in signal classification. Compared to SR, CR is more computationally efficient, but does not give obvious performance improvement. Therefore, we propose DCR, which aims at improving the performance of CR in signal classification. Besides, we extend DCR to Kernel DCR (KDCR), which generalizes DCR by introducing kernel functions. Comparisons among SR, CR and DCR are made in doing two audio signal classification tasks. Experimental results show that DCR can outperform CR and SR in both classification tasks, which demonstrates the effectiveness of our proposed DCR and the usefulness of the extra discriminative term.

Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages31-36
Number of pages6
ISBN (Electronic)9781538637883
DOIs
Publication statusPublished - 20 Aug 2018
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: 20 Aug 201824 Aug 2018

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2018-August
ISSN (Print)1051-4651

Conference

Conference24th International Conference on Pattern Recognition, ICPR 2018
CountryChina
CityBeijing
Period20/08/1824/08/18

Keywords

  • audio signal classification
  • collaborative representation
  • discriminative collaborative representation
  • Sparse representation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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