Kernel-based nonlinear feature extraction and classification algorithms
are a popular new research direction in machine learning. This paper
examines their applicability to the classification of phonemes in
a phonological awareness drilling software package. We first give
a concise overview of the nonlinear feature extraction methods such
as kernel principal component analysis (KPCA), kernel independent
component analysis (KICA), kernel linear discriminant analysis (KLDA)
and kernel springy discriminant analysis (KSDA). The overview deals
with all the methods in a unified framework, regardless of whether
they are unsupervised or supervised. The effect of the transformations
on a subsequent classification is tested in combination with learning
algorithms such as Gaussian mixture modeling (GMM), artificial neural
nets (ANN), projection pursuit learning (PPL), decision tree-based
classification (C4.5) and support vector machines (SVM). We found
in most cases that the transformations have a beneficial effect
on the classification performance. Furthermore, the nonlinear supervised
algorithms yielded the best results.
