Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction

Kai Qin; P.N. Suganthan; Marco Loog

Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction

Kai Qin, P.N. Suganthan, Marco Loog

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

22 Citationer (Scopus)

Abstract

Udgivelsesdato: September

Originalsprog	Engelsk
Tidsskrift	Pattern Recognition
Vol/bind	39
Udgave nummer	9
Sider (fra-til)	1805-1808
ISSN	0031-3203
Status	Udgivet - 2006
Udgivet eksternt	Ja

Bibliografisk note

Paper id:: 10.1016/j.patcog.2006.04.002

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Citationsformater

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title = "Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction",

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.",

author = "Kai Qin and P.N. Suganthan and Marco Loog",

note = "Paper id:: 10.1016/j.patcog.2006.04.002",

year = "2006",

language = "English",

volume = "39",

pages = "1805--1808",

journal = "Pattern Recognition",

issn = "0031-3203",

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T1 - Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction

AU - Qin, Kai

AU - Suganthan, P.N.

AU - Loog, Marco

N1 - Paper id:: 10.1016/j.patcog.2006.04.002

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

M3 - Journal article

SN - 0031-3203

VL - 39

SP - 1805

EP - 1808

JO - Pattern Recognition

JF - Pattern Recognition

IS - 9

ER -