Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria

Authors:
Marco Loog;R. P. W. Duin;R. Haeb-Umbach
Affiliations:
Univ. Medical Center Utrecht, Utrecht, The Netherlands;Delft Univ. of Technology, Delft, The Netherlands;Philips Research Laboratories Aachen, Aachen, Germany
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2001

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Abstract

We derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well-known K-class Fisher criterion associated with linear discriminant analysis (LDA). It can be seen that LDA weights contributions of individual class pairs according to the Euclidian distance of the respective class means. We generalize upon LDA by introducing a different weighting function.