Discriminative mixed membership models

Hanhuai Shan, Arindam Banerjee

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Although mixed membership models have achieved great success in unsupervised learning, they have not been applied as widely to classification problems. In this chapter, we discuss a family of discriminative mixed membership (DMM) models. By combining unsupervised mixed membership models with multi-class logistic regression, DMM models can be used for classification. In particular, we discuss discriminative latent Dirichlet allocation (DLDA) for text classification and discriminative mixed membership naive Bayes (DMNB) for classification on general feature vectors. Two variation inference algorithms are considered for learning the models, including a fast inference algorithm which uses fewer variational parameters and is substantially more efficient than the standard mean field variational approximation. The efficacy of the models is demonstrated by extensive experiments on multiple datasets.

Original languageEnglish (US)
Title of host publicationHandbook of Mixed Membership Models and Their Applications
PublisherCRC Press
Pages325-350
Number of pages26
ISBN (Electronic)9781466504097
ISBN (Print)9781466504080
DOIs
StatePublished - Jan 1 2014

Fingerprint Dive into the research topics of 'Discriminative mixed membership models'. Together they form a unique fingerprint.

  • Cite this

    Shan, H., & Banerjee, A. (2014). Discriminative mixed membership models. In Handbook of Mixed Membership Models and Their Applications (pp. 325-350). CRC Press. https://doi.org/10.1201/b17520