Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria (2001)
by
Marco Loog
,
R.P.W. Duin
,
R. Haeb-Umbach
| Venue: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Citations: | 42 - 2 self |
BibTeX
@ARTICLE{Loog01multiclasslinear,
author = {Marco Loog and R.P.W. Duin and R. Haeb-Umbach},
title = {Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2001},
volume = {23},
pages = {762--766}
}
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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.
