2011
An Information Geometrical View of Stationary Subspace Analysis
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2011, PT II
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- Volume
- 6792
- Number
- First page
- 397
- Last page
- 404
- Language
- English
- Publishing type
- Research paper (international conference proceedings)
- DOI
- 10.1007/978-3-642-21738-8_51
- Publisher
- SPRINGER-VERLAG BERLIN
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.
- Link information
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- DOI
- https://doi.org/10.1007/978-3-642-21738-8_51
- DBLP
- https://dblp.uni-trier.de/rec/conf/icann/KawanabeSBM11
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000296487200051&DestApp=WOS_CPL
- URL
- http://dblp.uni-trier.de/db/conf/icann/icann2011-2.html#conf/icann/KawanabeSBM11
- ID information
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- DOI : 10.1007/978-3-642-21738-8_51
- ISSN : 0302-9743
- eISSN : 1611-3349
- DBLP ID : conf/icann/KawanabeSBM11
- Web of Science ID : WOS:000296487200051