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2010 JMLR JMLR 2010

Classification with Incomplete Data Using Dirichlet Process Priors

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. [abs] [ pdf ][ bib ] © JMLR 2010. (edit, beta)

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
📈 Trend Setter — Meta-Learning
🐣 Hot Topic Early Bird — multi-task learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Chunping Wang , Xuejun Liao , Lawrence Carin , David B. Dunson

Topics

Artificial Intelligence > Learning Paradigms > Meta-Learning Machine Learning > Core Methods > Classification Machine Learning > Optimization & Theory > Bayesian Inference

Keywords

multi-task learning dirichlet process variational bayesian non-parametric bayesian mixture model incomplete datum
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