Covariate-dependent dictionary learning and sparse coding

Mingyuan Zhou; Hongxia Yang; Guillermo Sapiro; David Dunson; Lawrence Carin

doi:10.1109/ICASSP.2011.5947685

Covariate-dependent dictionary learning and sparse coding

Mingyuan Zhou, Hongxia Yang, Guillermo Sapiro, David Dunson, Lawrence Carin

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

2 Scopus citations

Abstract

A dependent hierarchical beta process (dHBP) is developed as a prior for data that may be represented in terms of a sparse set of latent features (dictionary elements), with covariate-dependent feature usage. The dHBP is applicable to general covariates and data models, imposing that signals with similar covariates are likely to be manifested in terms of similar features. As an application, we consider the simultaneous sparse modeling of multiple images, with the covariate of a given image linked to its similarity to all other images (as applied in manifold learning). Efficient inference is performed using hybrid Gibbs, Metropolis-Hastings and slice sampling.

Original language	English (US)
Title of host publication	2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages	5824-5827
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2011.5947685
State	Published - 2011
Externally published	Yes
Event	36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic Duration: May 22 2011 → May 27 2011

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Other

Other	36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/Territory	Czech Republic
City	Prague
Period	5/22/11 → 5/27/11

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

Keywords

Bayesian
covariates
dictionary learning
sparse coding

Access to Document

10.1109/ICASSP.2011.5947685

Cite this

Zhou, M., Yang, H., Sapiro, G., Dunson, D., & Carin, L. (2011). Covariate-dependent dictionary learning and sparse coding. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings (pp. 5824-5827). Article 5947685 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2011.5947685

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title = "Covariate-dependent dictionary learning and sparse coding",

abstract = "A dependent hierarchical beta process (dHBP) is developed as a prior for data that may be represented in terms of a sparse set of latent features (dictionary elements), with covariate-dependent feature usage. The dHBP is applicable to general covariates and data models, imposing that signals with similar covariates are likely to be manifested in terms of similar features. As an application, we consider the simultaneous sparse modeling of multiple images, with the covariate of a given image linked to its similarity to all other images (as applied in manifold learning). Efficient inference is performed using hybrid Gibbs, Metropolis-Hastings and slice sampling.",

keywords = "Bayesian, covariates, dictionary learning, sparse coding",

author = "Mingyuan Zhou and Hongxia Yang and Guillermo Sapiro and David Dunson and Lawrence Carin",

year = "2011",

doi = "10.1109/ICASSP.2011.5947685",

language = "English (US)",

isbn = "9781457705397",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

pages = "5824--5827",

booktitle = "2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings",

note = "36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 ; Conference date: 22-05-2011 Through 27-05-2011",

}

Zhou, M, Yang, H, Sapiro, G, Dunson, D & Carin, L 2011, Covariate-dependent dictionary learning and sparse coding. in 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings., 5947685, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 5824-5827, 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, Prague, Czech Republic, 5/22/11. https://doi.org/10.1109/ICASSP.2011.5947685

Covariate-dependent dictionary learning and sparse coding. / Zhou, Mingyuan; Yang, Hongxia; Sapiro, Guillermo et al.
2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings. 2011. p. 5824-5827 5947685 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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

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AU - Yang, Hongxia

AU - Sapiro, Guillermo

AU - Dunson, David

AU - Carin, Lawrence

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N2 - A dependent hierarchical beta process (dHBP) is developed as a prior for data that may be represented in terms of a sparse set of latent features (dictionary elements), with covariate-dependent feature usage. The dHBP is applicable to general covariates and data models, imposing that signals with similar covariates are likely to be manifested in terms of similar features. As an application, we consider the simultaneous sparse modeling of multiple images, with the covariate of a given image linked to its similarity to all other images (as applied in manifold learning). Efficient inference is performed using hybrid Gibbs, Metropolis-Hastings and slice sampling.

AB - A dependent hierarchical beta process (dHBP) is developed as a prior for data that may be represented in terms of a sparse set of latent features (dictionary elements), with covariate-dependent feature usage. The dHBP is applicable to general covariates and data models, imposing that signals with similar covariates are likely to be manifested in terms of similar features. As an application, we consider the simultaneous sparse modeling of multiple images, with the covariate of a given image linked to its similarity to all other images (as applied in manifold learning). Efficient inference is performed using hybrid Gibbs, Metropolis-Hastings and slice sampling.

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KW - covariates

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KW - sparse coding

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T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 5824

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Zhou M, Yang H, Sapiro G, Dunson D, Carin L. Covariate-dependent dictionary learning and sparse coding. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings. 2011. p. 5824-5827. 5947685. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2011.5947685

Covariate-dependent dictionary learning and sparse coding

Abstract

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All Science Journal Classification (ASJC) codes

Keywords

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