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| Zitatschlüssel | Kawanabe2011 |
|---|---|
| Autor | Kawanabe, M., Samek, W., von Bünau, P., and Meinecke, F. |
| Buchtitel | Artificial Neural Networks and Machine Learning – ICANN 2011 |
| Seiten | 397-404 |
| Jahr | 2011 |
| ISBN | 978-3-642-21737-1 |
| DOI | 10.1007/978-3-642-21738-8_51 |
| Jahrgang | 6792 |
| Herausgeber | Honkela, Timo and Duch, Wlodzislaw and Girolami, Mark and Kaski, Samuel |
| Verlag | Springer Berlin / Heidelberg |
| Serie | Lecture Notes in Computer Science |
| Zusammenfassung | 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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