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Variational Learning for Finite Dirichlet Mixture Models and Applications

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3 Author(s)
Wentao Fan ; Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, QC, Canada ; Bouguila, N. ; Ziou, D.

In this paper, we focus on the variational learning of finite Dirichlet mixture models. Compared to other algorithms that are commonly used for mixture models (such as expectation-maximization), our approach has several advantages: first, the problem of over-fitting is prevented; furthermore, the complexity of the mixture model (i.e., the number of components) can be determined automatically and simultaneously with the parameters estimation as part of the Bayesian inference procedure; finally, since the whole inference process is analytically tractable with closed-form solutions, it may scale well to large applications. Both synthetic and real data, generated from real-life challenging applications namely image databases categorization and anomaly intrusion detection, are experimented to verify the effectiveness of the proposed approach.

Published in:

Neural Networks and Learning Systems, IEEE Transactions on  (Volume:23 ,  Issue: 5 )

Date of Publication:

May 2012

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