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An information geometrical view of stationary subspace analysis
Citation key Kawanabe2011
Author Kawanabe, M., Samek, W., von Bünau, P., and Meinecke, F.
Title of Book Artificial Neural Networks and Machine Learning – ICANN 2011
Pages 397-404
Year 2011
ISBN 978-3-642-21737-1
DOI 10.1007/978-3-642-21738-8_51
Volume 6792
Editor Honkela, Timo and Duch, Wlodzislaw and Girolami, Mark and Kaski, Samuel
Publisher Springer Berlin / Heidelberg
Series Lecture Notes in Computer Science
Abstract Stationary Subspace Analysis (SSA) [3] is an unsupervised learning method that finds subspaces in which data distributions stay invariant over time. It has been shown to be very useful for studying non-stationarities in various applications [5,10,4,9]. In this paper, we present the first SSA algorithm based on a full generative model of the data. This new derivation relates SSA to previous work on finding interesting subspaces from high-dimensional data in a similar way as the three easy routes to independent component analysis [6], and provides an information geometric view.
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