Kernel Principal Component analysis through time for voice disorder classification.

Published

Journal Article

Kernel Principal Component analysis is a nonlinear 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.

Full Text

Duke Authors

Cited Authors

  • Alvarez, M; Henao, R; Castellanos, G; Godino, JI; Orozco, A

Published Date

  • 2006

Published In

Volume / Issue

  • 1 /

Start / End Page

  • 5511 - 5514

PubMed ID

  • 17946310

Pubmed Central ID

  • 17946310

International Standard Serial Number (ISSN)

  • 1557-170X

Digital Object Identifier (DOI)

  • 10.1109/IEMBS.2006.260357

Language

  • eng

Conference Location

  • United States