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<p><b>Abstract</b>—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.</p>
Feature selection, feature ordering, mixture distribution, maximum likelihood, EM algorithm, Kullback J-divergence.

P. Pudil, J. Novovicová and J. Kittler, "Divergence Based Feature Selection for Multimodal Class Densities," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 18, no. , pp. 218-223, 1996.
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