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Infinite latent feature models and the Indian buffet process

by: T. Griffiths, Z. Ghahramani
(2005)  Key: citeulike:1041305

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Abstract

We define a probability distribution over equivalence classes of 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.


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