Sparse tensor discriminant analysis

Zhihui Lai, Yong Xu, Jian Yang, Jinhui Tang, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

105 Citations (Scopus)

Abstract

The classical linear discriminant analysis has undergone great development and has recently been extended to different cases. In this paper, a novel discriminant subspace learning method called sparse tensor discriminant analysis (STDA) is proposed, which further extends the recently presented multilinear discriminant analysis to a sparse case. Through introducing the L1and L2norms into the objective function of STDA, we can obtain multiple interrelated sparse discriminant subspaces for feature extraction. As there are no closed-form solutions, k-mode optimization technique and the L1norm sparse regression are combined to iteratively learn the optimal sparse discriminant subspace along different modes of the tensors. Moreover, each non-zero element in each subspace is selected from the most important variables/factors, and thus STDA has the potential to perform better than other discriminant subspace methods. Extensive experiments on face databases (Yale, FERET, and CMU PIE face databases) and the Weizmann action database show that the proposed STDA algorithm demonstrates the most competitive performance against the compared tensor-based methods, particularly in small sample sizes.
Original languageEnglish
Article number6518139
Pages (from-to)3904-3915
Number of pages12
JournalIEEE Transactions on Image Processing
Volume22
Issue number10
DOIs
Publication statusPublished - 17 Sep 2013

Keywords

  • Face recognition
  • Feature extraction
  • Linear discriminant analysis
  • Sparse projections

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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