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

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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.

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Notes

  1. The complete source code of this work is available upon request.

  2. Source code of PCA-SIFT: http://www.cs.cmu.edu/~yke/pcasift.

  3. WordNet is a large lexical database for English, which groups English words into sets of cognitive synonyms called synsets.

  4. http://labelme.csail.mit.edu/.

  5. Datasets are available at: http://vision.eecs.ucf.edu/datasetsActions.html.

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Acknowledgments

The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors would like to thank the anonymous referees and the associate editor for their comments. The complete source code of this work is available upon request.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Wentao Fan

  2. The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada

    Nizar Bouguila

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  1. Wentao Fan
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  2. Nizar Bouguila
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Correspondence to Nizar Bouguila.

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Fan, W., Bouguila, N. Non-Gaussian Data Clustering via Expectation Propagation Learning of Finite Dirichlet Mixture Models and Applications. Neural Process Lett 39, 115–135 (2014). https://doi.org/10.1007/s11063-013-9293-x

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