A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Fundamentals of speech recognition
Fundamentals of speech recognition
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
New approximations of differential entropy for independent component analysis and projection pursuit
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Discriminative Segmental Speech Model and Its Application to Hungarian Number Recognition
TDS '00 Proceedings of the Third International Workshop on Text, Speech and Dialogue
A Nonlinearized Discriminant Analysis and Its Application to Speech Impediment Therapy
TSD '01 Proceedings of the 4th International Conference on Text, Speech and Dialogue
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Global and local preserving feature extraction for image categorization
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Kernel independent component analysis for gene expression data clustering
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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It is common practice to apply linear or nonlinear feature extraction methods before classification. Usually linear methods are faster and simpler than nonlinear ones but an idea successfully employed in the nonlinearization of Support Vector Machines permits a simple and effective extension of several statistical methods to their nonlinear counterparts. In this paper we follow this general nonlinearization approach in the context of Independent Component Analysis, which is a general purpose statistical method for blind source separation and feature extraction. In addition, nonlinearized formulae are furnished along with an illustration of the usefulness of the proposed method as an unsupervised feature extractor for the classification of Hungarian phonemes.