Divergence Maximizing Linear Projection for Supervised Dimension
Reduction
Divergence Maximizing Linear Projection for Supervised Dimension
Reduction
This paper proposes two linear projection methods for supervised dimension reduction using only the first and second-order statistics. The methods, each catering to a different parameter regime, are derived under the general Gaussian model by maximizing the Kullback-Leibler divergence between the two classes in the projected sample for a binary …