Multiclass linear dimension reduction by weighted pairwise Fisher criteria
IEEE Transactions on Pattern Analysis and Machine IntelligencePublished 1 July 2001
Marco Loog, Robert P. W. Duin, Reinhold Haeb‐Umbach
Citations456
SJR quartileQ1
SJR score3.91
SNIP5.99
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TL;DR
A class of computationally inexpensive linear dimension reduction criteria is derived by introducing a weighted variant of the well-known K-class Fisher criterion associated with linear discriminant analysis (LDA).
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 Euclidean distance of the respective class means. We generalize upon LDA by introducing a different weighting function.
Keywords
Computer ScienceMathematics
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