A variational Bayes model for count data learning and classification

• A latent generalized Dirichlet allocation model is proposed.
• The proposed model is learned using a principled variational approach.
• The model is applied to the challenging problems of visual scene and text categorization.

Several machine learning and knowledge discovery approaches have been proposed for count data modeling and classification. In particular, latent Dirichlet allocation (LDA) (Blei et al., 2003a) has received a lot of attention and has been shown to be extremely useful in several applications. Although the LDA is generally accepted to be one of the most powerful generative models, it is based on the Dirichlet assumption which has some drawbacks as we shall see in this paper. Thus, our goal is to enhance the LDA by considering the generalized Dirichlet distribution as a prior. The resulting generative model is named latent generalized Dirichlet allocation (LGDA) to maintain consistency with the original model. The LGDA is learned using variational Bayes which provides computationally tractable posterior distributions over the model׳s hidden variables and its parameters. To evaluate the practicality and merits of our approach, we consider two challenging applications namely text classification and visual scene categorization.