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2015 | OriginalPaper | Buchkapitel

Variational Learning of Finite Inverted Dirichlet Mixture Models and Applications

verfasst von : Parisa Tirdad, Nizar Bouguila, Djemel Ziou

Erschienen in: Artificial Intelligence Applications in Information and Communication Technologies

Verlag: Springer International Publishing

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Abstract

Statistical modeling provides a useful and well grounded framework to conduct inference from data. This has given rise to the development of varied rich suite of models and techniques. In particular, finite mixture models have received a lot of attention by offering a formal approach to unsupervised learning which allows to discover the latent structure expressed in observed data. In this chapter, we propose a mixture model based on the inverted Dirichlet mixture which provides a natural way of clustering positive data. An EM-style algorithm is developed based upen variational inference for learning the parameters of the mixture model. The proposed statistical framework is applied to the challenging tasks of natural scene categorization and human activity classification.

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Vorheriges Kapitel A Statistical Framework for Mental Targets Search Using Mixture Models

Nächstes Kapitel A Fully Bayesian Framework for Positive Data Clustering

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Titel: Variational Learning of Finite Inverted Dirichlet Mixture Models and Applications
verfasst von: Parisa Tirdad
Nizar Bouguila
Djemel Ziou
Verlag: Springer International Publishing
Buch: Artificial Intelligence Applications in Information and Communication Technologies
Print ISBN: 978-3-319-19832-3

Electronic ISBN: 978-3-319-19833-0

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-19833-0_6

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