Rapid and brief communication: Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction

Authors:
A. K. Qin;P. N. Suganthan;M. Loog
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798;The Image Group IT University of Copenhagen, Rued Langgaards Vej 7, 2300 Copenhagen S, Denmark
Venue:
Pattern Recognition
Year:
2006

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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.