Nikunj C. Oza's Publications

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Discriminative Mixed-membership Models

Discriminative Mixed-membership Models. Hanhuai Shan, Arindam Banerjee, and Nikunj Oza. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2009.

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Abstract

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 mixed membership 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 mixed membership 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 state of the art, and can discover components not explicitly captured by the class labels.

BibTeX Entry

@inproceedings{shba09,
	author = {Hanhuai Shan, Arindam Banerjee, and Nikunj Oza},
	title = {Discriminative Mixed-membership Models},
	booktitle={Proceedings of the IEEE International Conference on Data Mining (ICDM)},
	abstract={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 mixed membership 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 mixed membership 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 state of the art, and can discover components not explicitly captured by the class labels.},
	bib2html_pubtype = {Refereed Conference},
	bib2html_rescat = {Text Mining},
	year = {2009}
}

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