TY - GEN

T1 - Infinite latent feature models and the Indian buffet process

AU - Griffiths, Thomas L.

AU - Ghahramani, Zoubin

PY - 2005

Y1 - 2005

N2 - 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. We identify a simple generative process that results in the same distribution over equivalence classes, which we call the Indian buffet process. We illustrate the use of this distribution as a prior in an infinite latent feature model, deriving a Markov chain Monte Carlo algorithm for inference in this model and applying the algorithm to an image dataset.

AB - 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. We identify a simple generative process that results in the same distribution over equivalence classes, which we call the Indian buffet process. We illustrate the use of this distribution as a prior in an infinite latent feature model, deriving a Markov chain Monte Carlo algorithm for inference in this model and applying the algorithm to an image dataset.

UR - http://www.scopus.com/inward/record.url?scp=84864043341&partnerID=8YFLogxK

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M3 - Conference contribution

AN - SCOPUS:84864043341

SN - 9780262232531

T3 - Advances in Neural Information Processing Systems

SP - 475

EP - 482

BT - Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference

T2 - 2005 Annual Conference on Neural Information Processing Systems, NIPS 2005

Y2 - 5 December 2005 through 8 December 2005

ER -