Probabilistic Kernel Principal Component Analysis Through Time

  • Mauricio Alvarez
  • Ricardo Henao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


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.


Hide Markov Model Kernel Principal Component Analysis Principal Component Analysis Model Standard Principal Component Analysis Sustained Vowel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mauricio Alvarez
    • 1
  • Ricardo Henao
    • 2
  1. 1.Program of Electrical Engineering 
  2. 2.School of Electrical TechnologyUniversidad Tecnológica de PereiraColombia

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