Phoneme Classification Using Kernel Principal Component Analysis

A substantial number of linear and nonlinear feature space transformation methods have been proposed in recent years. Using the so-called `kernel-idea´ well-known linear techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) can be non-linearized in a general way. The aim of this paper here is twofold. First, we describe this general non-linearization technique for linear feature space transformation methods. Second, we derive formulas for the ubiquitous PCA technique and its kernel version, first proposed by Scholkopf et al., using this general schema and we examine how this transformation affects the efficiency of several learning algorithms applied to the phoneme classification task.