Discriminative Mixed-Membership Models

Although mixed-membership models have achieved great success in unsupervised learning, they have not been widely applied to classification problems. In this paper, we propose a family of discriminative mixed-membership models for classification by combining unsupervised mixedmembership models with multi-class logistic regression. In particular, we propose two variants respectively applicable to text classification based on latent Dirichlet allocation and usual feature vector classification based on mixedmembership naive Bayes models. The proposed models allow the number of components in the mixed membership to be different from the number of classes. We propose two variational inference based algorithms for learning the models, including a fast variational inference which is substantially more efficient than mean-field variational approximation. Through extensive experiments on UCI and text classification benchmark datasets, we show that the models are competitive with the stat...

• Type: Conference
• Year: 2009
• Where: ICDM
• Authors: Hanhuai Shan, Arindam Banerjee, Nikunj C. Oza
• Keywords: Data Mining, Discriminative Mixed-membership Models, ICDM 2009, Mixed-membership Models, Unsupervised Mixedmembership Models