Sparsity preserving embedding with manifold learning and discriminant analysis

Qian Liu, Chao Lan, Xiao Yuan Jing, Shi Qiang Gao, Dapeng Zhang, Jing Yu Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

In the past few years, discriminant analysis and manifold learning have been widely used in feature extraction. Recently, the sparse representation technique has advanced the development of pattern recognition. In this paper, we combine both discriminant analysis and manifold learning with sparse representation technique and propose a novel feature extraction approach named sparsity preserving embedding with manifold learning and discriminant analysis. It seeks an embedded space, where not only the sparse reconstructive relations among original samples are preserved, but also the manifold and discriminant information of both original sample set and the corresponding reconstructed sample set is maintained. Experimental results on the public AR and FERET face databases show that our approach outperforms relevant methods in recognition performance.
Original languageEnglish
Pages (from-to)271-274
Number of pages4
JournalIEICE Transactions on Information and Systems
VolumeE-95-D
Issue number1
DOIs
Publication statusPublished - 1 Jan 2012

Keywords

  • Discriminant analysis
  • Feature extraction
  • Manifold learning
  • Sparsity preserving embedding

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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