Extracting latent dynamics from multi-dimensional data by probabilistic slow feature analysis

Toshiaki Omori

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

Abstract

Slow feature analysis (SFA) is a time-series analysis method for extracting slowly-varying latent features from multi-dimensional data. In this paper, the probabilistic version of SFA algorithms is discussed from a theoretical point of view. First, the fundamental notions of SFA algorithms are reviewed in order to show the mechanism of extracting the slowly-varying latent features by means of the SFA. Second, recent advances in the SFA algorithms are described on the emphasis of the probabilistic version of the SFA. Third, the probabilistic SFA with rigorously derived likelihood function is derived by means of belief propagation. Using the rigorously derived likelihood function, we simultaneously extracts slow features and underlying parameters for the latent dynamics. Finally, we show using synthetic data that the probabilistic SFA with rigorously derived likelihood function can estimate the slow feature accurately even under noisy environments.

Original languageEnglish
Title of host publicationNeural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Pages108-116
Number of pages9
EditionPART 3
DOIs
Publication statusPublished - 2013 Dec 1
Event20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of
Duration: 2013 Nov 32013 Nov 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8228 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Neural Information Processing, ICONIP 2013
CountryKorea, Republic of
CityDaegu
Period13/11/313/11/7

Keywords

  • Bayesian statistics
  • Latent dynamics
  • Probabilistic information processing
  • Slow feature analysis
  • State-space model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Omori, T. (2013). Extracting latent dynamics from multi-dimensional data by probabilistic slow feature analysis. In Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings (PART 3 ed., pp. 108-116). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8228 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-42051-1_15