Infinite latent feature models and the Indian buffet process

Thomas L. Griffiths; Zoubin Ghahramani

Infinite latent feature models and the Indian buffet process

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. 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.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference
Pages	475-482
Number of pages	8
State	Published - Dec 1 2005
Externally published	Yes
Event	2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada Duration: Dec 5 2005 → Dec 8 2005

Other

Other	2005 Annual Conference on Neural Information Processing Systems, NIPS 2005
Country/Territory	Canada
City	Vancouver, BC
Period	12/5/05 → 12/8/05

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

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title = "Infinite latent feature models and the Indian buffet process",

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. 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.",

author = "Griffiths, {Thomas L.} and Zoubin Ghahramani",

year = "2005",

month = dec,

day = "1",

language = "English (US)",

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pages = "475--482",

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note = "2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 ; Conference date: 05-12-2005 Through 08-12-2005",

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T1 - Infinite latent feature models and the Indian buffet process

AU - Griffiths, Thomas L.

AU - Ghahramani, Zoubin

PY - 2005/12/1

Y1 - 2005/12/1

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

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

M3 - Conference contribution

AN - SCOPUS:84864043341

SN - 9780262232531

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 -

Infinite latent feature models and the Indian buffet process

Abstract

Other

All Science Journal Classification (ASJC) codes

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