On the integration of topic modeling and dictionary learning

Lingbo Li; Mingyuan Zhou; Guillermo Sapiro; Lawrence Carin

On the integration of topic modeling and dictionary learning

Lingbo Li, Mingyuan Zhou, Guillermo Sapiro, Lawrence Carin

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

Abstract

A new nonparametric Bayesian model is developed to integrate dictionary learning and topic model into a unified framework. The model is employed to analyze partially annotated images, with the dictionary learning performed directly on image patches. Efficient inference is performed with a Gibbs-slice sampler, and encouraging results are reported on widely used datasets. 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	625-632
Number of pages	8
State	Published - Oct 7 2011
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09

Cite this

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title = "On the integration of topic modeling and dictionary learning",

abstract = "A new nonparametric Bayesian model is developed to integrate dictionary learning and topic model into a unified framework. The model is employed to analyze partially annotated images, with the dictionary learning performed directly on image patches. Efficient inference is performed with a Gibbs-slice sampler, and encouraging results are reported on widely used datasets. Copyright 2011 by the author(s)/owner(s).",

author = "Lingbo Li and Mingyuan Zhou and Guillermo Sapiro and Lawrence Carin",

note = "Generated from Scopus record by KAUST IRTS on 2021-02-09",

year = "2011",

month = oct,

day = "7",

language = "English (US)",

isbn = "9781450306195",

pages = "625--632",

booktitle = "Proceedings of the 28th International Conference on Machine Learning, ICML 2011",

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AU - Li, Lingbo

AU - Zhou, Mingyuan

AU - Sapiro, Guillermo

AU - Carin, Lawrence

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2011/10/7

Y1 - 2011/10/7

N2 - A new nonparametric Bayesian model is developed to integrate dictionary learning and topic model into a unified framework. The model is employed to analyze partially annotated images, with the dictionary learning performed directly on image patches. Efficient inference is performed with a Gibbs-slice sampler, and encouraging results are reported on widely used datasets. Copyright 2011 by the author(s)/owner(s).

AB - A new nonparametric Bayesian model is developed to integrate dictionary learning and topic model into a unified framework. The model is employed to analyze partially annotated images, with the dictionary learning performed directly on image patches. Efficient inference is performed with a Gibbs-slice sampler, and encouraging results are reported on widely used datasets. Copyright 2011 by the author(s)/owner(s).

UR - http://www.scopus.com/inward/record.url?scp=80053450173&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781450306195

SP - 625

EP - 632

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

ER -

On the integration of topic modeling and dictionary learning

Abstract

Bibliographical note

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