Parsimonious Kernel Fisher Discrimination

Authors:
Kitsuchart Pasupa;Robert F. Harrison;Peter Willett
Affiliations:
Department of Automatic Control & Systems Engineering,;Department of Automatic Control & Systems Engineering,;Department of Information Studies, The University of Sheffield, UK
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Year:
2007

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Abstract

By applying recent results in optimization transfer, a new algorithm for kernel Fisher Discriminant Analysis is provided that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The algorithm is simple, easily programmed and is shown to perform as well as or better than a number of leading machine learning algorithms on a substantial benchmark. It is then applied to a set of extreme small-sample-size problems in virtual screening where it is found to be less accurate than a currently leading approach but is still comparable in a number of cases.