Probabilistic kernel principal component analysis through time

Conference Paper

This paper introduces a temporal version of Probabilistic Kernel Principal Component Analysis by using a hidden Markov model in order to obtain optimized representations of observed data through time. Recently introduced. Probabilistic Kernel Principal Component Analysis overcomes the two main disadvantages of standard Principal Component Analysis, namely, absence of probability density model and lack of high-order statistical information due to its linear structure. We extend this probabilistic approach of KPCA to mixture models in time, to enhance the capabilities of transformation and reduction of time series vectors. Results over voice disorder databases show improvements in classification accuracies even with highly reduced representations. © Springer-Verlag Berlin Heidelberg 2006.

Full Text

Duke Authors

Cited Authors

  • Alvarez, M; Henao, R

Published Date

  • January 1, 2006

Published In

Volume / Issue

  • 4232 LNCS /

Start / End Page

  • 747 - 754

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

International Standard Book Number 10 (ISBN-10)

  • 3540464794

International Standard Book Number 13 (ISBN-13)

  • 9783540464792

Digital Object Identifier (DOI)

  • 10.1007/11893028_83

Citation Source

  • Scopus