Sixth International Conference on Spoken Language Processing
October 16-20, 2000
Multi-Class Linear Dimension Reduction by Generalized Fisher Criteria
Marco Loog (2,1), Reinhold Haeb-Umbach (1)
(1) Philips Research Laboratories Aachen, Germany
Linear Disciminant Analysis is in general unable to find the
lower-dimensional feature space which maximizes the class discrimination,
even if the class distributions can be assumed to be
very simple, e.g. Gaussians with identical covariance matrices.
In this paper we reformulate the K-class Fisher criterion as a
sum of K(K-1)/2 two-class Fisher criteria. This formulation
allows to weigh class pair contributions according to their relevance
for classification. Further it offers an obvious way how
to cope with heteroscedastic models. We propose a particular
weighting scheme which attempts to approximate the pairwise
Bayes error. Moderate improvements are obtained on the TIMIT
phoneme classification task.
(2) SSOR, Faculty of Information Technology and Systems,
Delft University of Technology, The Netherlands
Loog, Marco / Haeb-Umbach, Reinhold (2000):
"Multi-class linear dimension reduction by generalized Fisher criteria",
In ICSLP-2000, vol.2, 1069-1072.