TY - GEN
T1 - Extracting latent dynamics from multi-dimensional data by probabilistic slow feature analysis
AU - Omori, Toshiaki
PY - 2013/12/1
Y1 - 2013/12/1
N2 - 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.
AB - 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.
KW - Bayesian statistics
KW - Latent dynamics
KW - Probabilistic information processing
KW - Slow feature analysis
KW - State-space model
UR - http://www.scopus.com/inward/record.url?scp=84893405443&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893405443&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-42051-1_15
DO - 10.1007/978-3-642-42051-1_15
M3 - Conference contribution
AN - SCOPUS:84893405443
SN - 9783642420504
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 108
EP - 116
BT - Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
T2 - 20th International Conference on Neural Information Processing, ICONIP 2013
Y2 - 3 November 2013 through 7 November 2013
ER -