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Divergence based feature selection for multimodal class densities

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3 Author(s)
J. Novovicova ; Inst. of Inf. Theory & Autom., Czechoslovak Acad. of Sci., Prague, Czech Republic ; P. Pudil ; J. Kittler

A new feature selection procedure based on the Kullback J-divergence between two class conditional density functions approximated by a finite mixture of parameterized densities of a special type is presented. This procedure is suitable especially for multimodal data. Apart from finding a feature subset of any cardinality without involving any search procedure, it also simultaneously yields a pseudo-Bayes decision rule. Its performance is tested on real data

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:18 ,  Issue: 2 )