Kernel Principal component analysis through time for voice disorder classification

Mauricio Alvarez, Ricardo Henao, Germán Castellanos, Juan I. Godino, Alvaro Orozco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Kernel Principal Component analysis is a non-linear generalization of the popular linear multivariate analysis method. However, this method assumes that the observed data is independent, a disadvantage for many practical applications. In order to overcome this difficulty, the authors propose a combination of Kernel Principal Component analysis and hidden Markov models. The novelty of the proposed method consists mainly in the way in which a static dimensionality reduction technique has been combined with a classic mixture model in time, to enhance the capabilities of transformation, reduction and classification of voice disorder data. Experimental results show improvements in classification accuracies even with highly reduced representations of the two databases used. © 2006 IEEE.
Original languageEnglish (US)
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Number of pages4
StatePublished - Dec 1 2006
Externally publishedYes


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