ISCA Archive ICSLP 2000
ISCA Archive ICSLP 2000

Multi-class linear dimension reduction by generalized Fisher criteria

Marco Loog, Reinhold Haeb-Umbach

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.


Cite as: Loog, M., Haeb-Umbach, R. (2000) Multi-class linear dimension reduction by generalized Fisher criteria. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 2, 1069-1072

@inproceedings{loog00_icslp,
  author={Marco Loog and Reinhold Haeb-Umbach},
  title={{Multi-class linear dimension reduction by generalized Fisher criteria}},
  year=2000,
  booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
  pages={vol. 2, 1069-1072}
}