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http://dx.doi.org/10.5351/KJAS.2015.28.2.251

Introduction to the Indian Buffet Process: Theory and Applications  

Lee, Youngseon (Department of Statistics, Seoul National University)
Lee, Kyoungjae (Department of Statistics, Seoul National University)
Lee, Kwangmin (Department of Statistics, Seoul National University)
Lee, Jaeyong (Department of Statistics, Seoul National University)
Seo, Jinwook (Department of Computer Science and Engineering, Seoul National University)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.2, 2015 , pp. 251-267 More about this Journal
Abstract
The Indian Buffet Process is a stochastic process on equivalence classes of binary matrices having finite rows and infinite columns. The Indian Buffet Process can be imposed as the prior distribution on the binary matrix in an infinite feature model. We describe the derivation of the Indian buffet process from a finite feature model, and briefly explain the relation between the Indian buffet process and the beta process. Using a Gaussian linear model, we describe three algorithms: Gibbs sampling algorithm, Stick-breaking algorithm and variational method, with application for finding features in image data. We also illustrate the use of the Indian Buffet Process in various type of analysis such as dyadic data analysis, network data analysis and independent component analysis.
Keywords
Indian buffet process; latent feature model; Gaussian linear model; Gibbs sampling; stick-breaking sampling; variational method;
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