Feature Selection Through Message Passing

Abstract

A novel similarity-based feature selection algorithm is developed, using the concept of distance correlation. A feature subset is selected in terms of this similarity measure between pairs of features, without assuming any underlying distribution of the data. The pair-wise similarity is then employed, in a message passing framework, to select a set of exemplars features involving minimum redundancy and reduced parameter tuning. The algorithm does not need an exhaustive traversal of the search space. The methodology is next extended to handle large data, using an inherent property of distance correlation. The effectiveness of the algorithm is demonstrated on nine sets of publicly-available data.