15593377

Source:http://linkedlifedata.com/resource/pubmed/id/15593377

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0005129, umls-concept:C0036679, umls-concept:C0085862, umls-concept:C0201519, umls-concept:C0237868, umls-concept:C0439962, umls-concept:C0449416, umls-concept:C0449432, umls-concept:C0677520, umls-concept:C0725066, umls-concept:C0936012, umls-concept:C1179435, umls-concept:C1299583, umls-concept:C1524073, umls-concept:C1548799, umls-concept:C1549571, umls-concept:C1561605, umls-concept:C1561606, umls-concept:C1608386, umls-concept:C1705248, umls-concept:C1857941
pubmed:issue	5
pubmed:dateCreated	2004-12-13
pubmed:abstractText	In this paper, we review recent advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixing models. After a general introduction to BSS and ICA, we discuss in more detail uniqueness and separability issues, presenting some new results. A fundamental difficulty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they provide non-unique solutions without extra constraints, which are often implemented by using a suitable regularization. In this paper, we explore two possible approaches. The first one is based on structural constraints. Especially, post-nonlinear mixtures are an important special case, where a nonlinearity is applied to linear mixtures. For such mixtures, the ambiguities are essentially the same as for the linear ICA or BSS problems. The second approach uses Bayesian inference methods for estimating the best statistical parameters, under almost unconstrained models in which priors can be easily added. In the later part of this paper, various separation techniques proposed for post-nonlinear mixtures and general nonlinear mixtures are reviewed.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/9100527
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Oct
pubmed:issn	0129-0657
pubmed:author	pubmed-author:JuttenChristianC, pubmed-author:KarhunenJuhaJ
pubmed:issnType	Print
pubmed:volume	14
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	267-92
pubmed:dateRevised	2006-11-15
pubmed:meshHeading	pubmed-meshheading:15593377-Bayes Theorem, pubmed-meshheading:15593377-Neural Networks (Computer), pubmed-meshheading:15593377-Nonlinear Dynamics, pubmed-meshheading:15593377-Principal Component Analysis, pubmed-meshheading:15593377-Signal Processing, Computer-Assisted
pubmed:year	2004
pubmed:articleTitle	Advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixtures.
pubmed:affiliation	Laboratory of Images and Signals (UMR CNRS 5083, INPG, UJF), 46 avenue Félix Viallet, 38031 Grenoble Cedex, France. Christian.Jutten@inpg.fr
pubmed:publicationType	Journal Article, Comparative Study, Research Support, Non-U.S. Gov't