Projective dictionary pair learning for pattern classification

Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng

Research output: Journal article publicationConference articleAcademic researchpeer-review

234 Citations (Scopus)

Abstract

Discriminative dictionary learning (DL) has been widely studied in various pattern classification problems. Most of the existing DL methods aim to learn a synthesis dictionary to represent the input signal while enforcing the representation coefficients and/or representation residual to be discriminative. However, the ℓ0 or ℓ1-norm sparsity constraint on the representation coefficients adopted in most DL methods makes the training and testing phases time consuming. We propose a new discriminative DL framework, namely projective dictionary pair learning (DPL), which learns a synthesis dictionary and an analysis dictionary jointly to achieve the goal of signal representation and discrimination. Compared with conventional DL methods, the proposed DPL method can not only greatly reduce the time complexity in the training and testing phases, but also lead to very competitive accuracies in a variety of visual classification tasks.
Original languageEnglish
Pages (from-to)793-801
Number of pages9
JournalAdvances in Neural Information Processing Systems
Volume1
Issue numberJanuary
Publication statusPublished - 1 Jan 2014
Event28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada
Duration: 8 Dec 201413 Dec 2014

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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