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 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.
Complete Metadata
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| accessLevel | public |
| accrualPeriodicity | irregular |
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| description | 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. |
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| identifier | DASHLINK_475 |
| issued | 2011-10-10 |
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| landingPage | https://c3.nasa.gov/dashlink/resources/475/ |
| modified | 2025-03-31 |
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| title | Discriminative Mixed-Membership Models |
