Tree-structured infinite sparse factor model

Xian Xing Zhang; David B. Dunson; Lawrence Carin

Tree-structured infinite sparse factor model

Xian Xing Zhang, David B. Dunson, Lawrence Carin

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

Abstract

A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a tree-based factor-analysis model. This new model is coupled with the nested Chinese restaurant process, to nonparametrically infer the depth and width (structure) of the tree. In addition to developing the model, theoretical properties of the TMGP are addressed, and a novel MCMC sampler is developed. The structure of the inferred tree is used to learn relationships between high-dimensional data, and the model is also applied to compressive sensing and interpolation of incomplete images. Copyright 2011 by the author(s)/owner(s).

Original language	English (US)
Title of host publication	Proceedings of the 28th International Conference on Machine Learning, ICML 2011
Pages	785-792
Number of pages	8
State	Published - Oct 7 2011
Externally published	Yes

Cite this

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title = "Tree-structured infinite sparse factor model",

abstract = "A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a tree-based factor-analysis model. This new model is coupled with the nested Chinese restaurant process, to nonparametrically infer the depth and width (structure) of the tree. In addition to developing the model, theoretical properties of the TMGP are addressed, and a novel MCMC sampler is developed. The structure of the inferred tree is used to learn relationships between high-dimensional data, and the model is also applied to compressive sensing and interpolation of incomplete images. Copyright 2011 by the author(s)/owner(s).",

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PY - 2011/10/7

Y1 - 2011/10/7

N2 - A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a tree-based factor-analysis model. This new model is coupled with the nested Chinese restaurant process, to nonparametrically infer the depth and width (structure) of the tree. In addition to developing the model, theoretical properties of the TMGP are addressed, and a novel MCMC sampler is developed. The structure of the inferred tree is used to learn relationships between high-dimensional data, and the model is also applied to compressive sensing and interpolation of incomplete images. Copyright 2011 by the author(s)/owner(s).

AB - A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a tree-based factor-analysis model. This new model is coupled with the nested Chinese restaurant process, to nonparametrically infer the depth and width (structure) of the tree. In addition to developing the model, theoretical properties of the TMGP are addressed, and a novel MCMC sampler is developed. The structure of the inferred tree is used to learn relationships between high-dimensional data, and the model is also applied to compressive sensing and interpolation of incomplete images. Copyright 2011 by the author(s)/owner(s).

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M3 - Conference contribution

SN - 9781450306195

SP - 785

EP - 792

BT - Proceedings of the 28th International Conference on Machine Learning, ICML 2011

ER -

Tree-structured infinite sparse factor model

Abstract

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