In Search of Non-Gaussian Components of a High-Dimensional Distribution
The Journal of Machine Learning Research
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Sparse non-Gaussian component analysis
IEEE Transactions on Information Theory
Colored subspace analysis: dimension reduction based on a signal's autocorrelation structure
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Stationary subspace analysis as a generalized eigenvalue problem
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
The Stationary Subspace Analysis Toolbox
The Journal of Machine Learning Research
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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. 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.