Title :
Multiclass linear dimension reduction by weighted pairwise Fisher criteria
Author :
Loog, Marco ; Duin, R.P.W. ; Haeb-Umbach, R.
Author_Institution :
Image Sci. Inst., Univ. Med. Center, Utrecht, Netherlands
fDate :
7/1/2001 12:00:00 AM
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 :
Bayes methods; error statistics; pattern classification; statistical analysis; Bayes error; Euclidean distance; Fisher criterion; approximate pairwise accuracy; linear dimension reduction; linear discriminant analysis; statistical pattern classification; weighting function; Computer Society; Computer networks; Eigenvalues and eigenfunctions; Iterative methods; Linear discriminant analysis; Maximum likelihood estimation; Neural networks; Parameter estimation; Scattering; State estimation;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
