Robust kernel discriminant analysis and its application to feature extraction and recognition

Zhizheng Liang, Dapeng Zhang, Pengfei Shi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

Subspace analysis is an effective technique for dimensionality reduction, which aims at finding a low-dimensional space of high-dimensional data. In this paper, a novel subspace method called robust kernel discriminant analysis is proposed for dimensionality reduction. An optimization function is firstly defined in terms of the distance between similar elements and the distance between dissimilar elements, which can preserve the structure of the data in the mapping space. Then the optimization function is transformed into an eigenvalue problem and the projection vectors are obtained by solving the eigenvalue problem. Finally, experimental results on face images and handwritten numerical characters demonstrate the effectiveness and feasibility of the proposed method.
Original languageEnglish
Pages (from-to)928-933
Number of pages6
JournalNeurocomputing
Volume69
Issue number7-9 SPEC. ISS.
DOIs
Publication statusPublished - 1 Mar 2006

Keywords

  • Character recognition
  • Dimensionality reduction
  • Face recognition
  • Kernel trick
  • Robust kernel discriminant analysis
  • Subspace analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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