Maximally discriminative spectral feature projections using mutual information
Umut Özertem, D. Erdogmus · Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. · 2006
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.