An Information Geometrical View of Stationary Subspace Analysis
Stationary Subspace Analysis (SSA)  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 , and provides an information geometric view.