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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2014

01.04.2014

Non-Gaussian Data Clustering via Expectation Propagation Learning of Finite Dirichlet Mixture Models and Applications

verfasst von: Wentao Fan, Nizar Bouguila

Erschienen in: Neural Processing Letters | Ausgabe 2/2014

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Abstract

Learning appropriate statistical models is a fundamental data analysis task which has been the topic of continuing interest. Recently, finite Dirichlet mixture models have proved to be an effective and flexible model learning technique in several machine learning and data mining applications. In this article, the problem of learning and selecting finite Dirichlet mixture models is addressed using an expectation propagation (EP) inference framework. Within the proposed EP learning method, for finite mixture models, all the involved parameters and the model complexity (i.e. the number of mixture components), can be evaluated simultaneously in a single optimization framework. Extensive simulations using synthetic data along with two challenging real-world applications involving automatic image annotation and human action videos categorization demonstrate that our approach is able to achieve better results than comparable techniques.
Nächster Artikel Approximation and Estimation Bounds for Subsets of Reproducing Kernel Kreǐn Spaces

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Fußnoten
1
The complete source code of this work is available upon request.
 
3
WordNet is a large lexical database for English, which groups English words into sets of cognitive synonyms called synsets.
 
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Metadaten
Titel
Non-Gaussian Data Clustering via Expectation Propagation Learning of Finite Dirichlet Mixture Models and Applications
verfasst von
Wentao Fan
Nizar Bouguila
Publikationsdatum
01.04.2014
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2014
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-013-9293-x

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