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2011 JMLR JMLR 2011

The Indian Buffet Process: An Introduction and Review

Abstract

The Indian buffet process is a stochastic process defining a probability distribution over equivalence classes of sparse binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features, or that involve bipartite graphs in which the size of at least one class of nodes is unknown. We give a detailed derivation of this distribution, and illustrate its use as a prior in an infinite latent feature model. We then review recent applications of the Indian buffet process in machine learning, discuss its extensions, and summarize its connections to other stochastic processes. [abs] [ pdf ][ bib ] © JMLR 2011. (edit, beta)

🧭 Keyword Pioneer — sparse binary matrix
🐣 Hot Topic Early Bird — stochastic process
🐝 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

Thomas L. Griffiths , Zoubin Ghahramani

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Optimization & Theory > Bayesian Inference Machine Learning > Optimization & Theory > Stochastic Processes

Keywords

bayesian inference indian buffet process stochastic process latent feature model sparse binary matrix
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