@article{,
author={Kocsor, Andr{\'a}s and Kuba, Andr{\'a}s Jr. and T{\'o}th, L{\'a}szl{\'o}},
title={Phoneme Classification Using Kernel Principal Component Analysis},
abstract={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 Sch{\"o}lkopf
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. },
journal={Periodica Polytechnica},
volume={44},
year={2000},
pages={77-90},
number={1}
}