Reference : Self-organizing mixture models
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/11042
Self-organizing mixture models
English
Verbeek, J. J. [> >]
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Krose, B. J. A. [> >]
2005
Neurocomputing
Elsevier Science
63
99-123
Yes (verified by ORBilu)
0925-2312
Amsterdam
The Netherlands
[en] self-organizing maps ; mixture model ; EM algorithm
[en] We present an expectation-maximization (EM) algorithm that yields topology preserving maps of data based on probabilistic mixture models. Our approach is applicable to any mixture model for which we have a normal EM algorithm. Compared to other mixture model approaches to self-organizing maps (SOMs), the function our algorithm maximizes has a clear interpretation: it sums data log-likelihood and a penalty term that enforces self-organization. Our approach allows principled handling of missing data and learning of mixtures of SOMs. We present example applications illustrating our approach for continuous, discrete, and mixed discrete and continuous data. (C) 2004 Elsevier B.V. All rights reserved.
http://hdl.handle.net/10993/11042

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