Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction

Kai Qin, P.N. Suganthan, Marco Loog

Research output: Contribution to journalJournal articleResearchpeer-review

21 Citations (Scopus)

Abstract

We propose a generalized null space uncorrelated Fisher discriminant analysis (GNUFDA) technique integrating the uncorrelated discriminant analysis and weighted pairwise Fisher criterion. The GNUFDA can effectively deal with the small sample-size problem and perform satisfactorily when the dimensionality of the null space decreases with increase in the number of training samples per class and/or classes, C. The proposed GNUFDA can extract at most C-1 optimal uncorrelated discriminative vectors without being influenced by the null-space dimensionality.
Original languageEnglish
JournalPattern Recognition
Volume39
Issue number9
Pages (from-to)1805-1808
ISSN0031-3203
Publication statusPublished - 2006
Externally publishedYes

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