On the application of probabilistic distance measures for the extraction of features from imperfectly labeled patterns

A commonly used approach for feature selection is to select those features that extremize certain probabilistic distance measures. In most of the procedures it is assumed that the labels of the patterns are perfect. There are many practical situations in which the labels of the patterns are imperfect. This paper examines the applicability of the extremization of the Bhattacharyya distance, the divergence, equivocation, Kalmogrov variational distance, and Matusita distance as criteria for selecting the effective features from imperfectly identified patterns.