Multiclass linear dimension reduction by weighted pairwise Fisher criteria
Marco Loog, Robert P. W. Duin, Reinhold Haeb‐Umbach · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2001
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.