Jointly Informative Feature Selection

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Leonidas Lefakis, Francois Fleuret ;
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:567-575, 2014.

Abstract

We propose several novel criteria for the selection of groups of jointly informative continuous features in the context of classification. Our approach is based on combining a Gaussian modeling of the feature responses, with derived upper bounds on their mutual information with the class label and their joint entropy. We further propose specific algorithmic implementations of these criteria which reduce the computational complexity of the algorithms by up to two-orders of magnitude, making these strategies tractable in practice. Experiments on multiple computer-vision data-bases, and using several types of classifiers, show that this class of methods outperforms state-of-the-art baselines, both in terms of speed and classification accuracy.

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