Reference : Self-organizing mixture models
Scientific journals : Article
Engineering, computing & technology : Computer science
Self-organizing mixture models
Verbeek, J. J. [> >]
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Krose, B. J. A. [> >]
Elsevier Science
Yes (verified by ORBilu)
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.

File(s) associated to this reference

Fulltext file(s):

Open access
download.pdfAuthor preprint859.02 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.