An Information Geometrical View of Stationary Subspace Analysis
Motoaki Kawanabe, Wojciech Samek, Paul Buenau and Frank Meinecke
In: Artificial Neural Networks and Machine Learning - ICANN 2011 Lecture Notes in Computer Science , 6792 . (2011) Springer Berlin / Heidelberg , pp. 397-404. ISBN 978-3-642-21737-1

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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