Work

Classification with Incomplete Data Using Dirichlet Process Priors.

Year: 2010

Type: article

Abstract: A non-parametric hierarchical Bayesian framework is developed for designing a classifier, based on a mixture of simple (linear) classifiers. Each simple classifier is termed a local "expert", and the number of experts and their construction are manifested via a Dirichlet process formulation. The simple form of the "experts" allows analytical handling of incomplete data. The model is extended to allow simultaneous design of classifiers on multiple data sets, termed multi-task learning, with this also performed non-parametrically via the Dirichlet process. Fast inference is performed using variational Bayesian (VB) analysis, and example results are presented for several data sets. We also perform inference via Gibbs sampling, to which we compare the VB results.

Source: PubMed

Authors: Chunping Wang, Xuejun Liao, Lawrence Carin, David B Dunson

Institution: Duke University

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Institute of Environmental Health Sciences

Awards: Nonparametric Bayes Methods for Biomedical Studies, Bayesian Methods for Assessing Gene by Environment Interactions

OpenAlex ID: W2168152978

FWCI: 2.031

Cites: 58

Cited by: 24

Related to: 10