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

Toshiaki Omori

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
ページ108-116
ページ数9
PART 3
DOI
出版ステータスPublished - 2013 12 1
イベント20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of
継続期間: 2013 11 32013 11 7

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 3
8228 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

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

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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