Abstract
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.
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