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Bayesian Independent Component Analysis with prior constraints: An application in biosignal analysis

Abstract:

In many data-driven machine learning problems it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make are then taken to be related to these sources through some unknown functionaility. Furthermore, the (unknown) number of underlying latent sources may be different to the number of observations and hence issues of model complexity plague the analysis. Recent developments in Independent Component Analysis (ICA) have shown tha...

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Publication status:
Published

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Publisher copy:
10.1007/11559887_10
Journal:
DETERMINISTIC AND STATISTICAL METHODS IN MACHINE LEARNING More from this journal
Volume:
3635
Pages:
159-179
Publication date:
2005-01-01
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
Language:
English
Keywords:
Pubs id:
pubs:318977
UUID:
uuid:8b3022f5-58f8-4589-9b3a-5f42985112da
Local pid:
pubs:318977
Source identifiers:
318977
Deposit date:
2013-11-17

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