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 language | English |
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Journal | Pattern Recognition |
Volume | 39 |
Issue number | 9 |
Pages (from-to) | 1805-1808 |
ISSN | 0031-3203 |
Publication status | Published - 2006 |
Externally published | Yes |