Maximally discriminative spectral feature projections using mutual information

Determining the optimal subspace projections, which maintains the best representation of the original data, is an important problem in machine learning and pattern recognition. In this paper, we propose a nonparametric nonlinear subspace projection technique that employs kernel density estimation based information theoretic methods and kernel machines, in order to maintain class separability maximally under the Shannon mutual information criterion.