Subspace Kernel Discriminant Analysis for Speech Recognition
Robust 2004 Workshop
Kernel Discriminant Analysis (KDA) has been successfully applied to many pattern recognition problems. KDA transforms the original problem into a space of dimension N where N is the number of training vectors. For speech recognition, N is usually prohibitively high increasing computational requirements beyond current computational capabilities. In this paper, we provide a formulation of a subspace version of KDA that enables its application to speech recognition, thus conveniently enabling nonlinear feature space transformations that result in discriminatory lower dimensional features.