Kernelized Sparse Bayesian Matrix Factorization.

Li, C; Xie, H-B; Fan, X; Xu, RYD; Van Huffel, S; Mengersen, K

Kernelized Sparse Bayesian Matrix Factorization.

Li, C

Xie, H-B Fan, X Xu, RYD Van Huffel, S Mengersen, K

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE transactions on neural networks and learning systems, 2021, 32, (1), pp. 391-404
Issue Date:: 2021-01-04

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Field	Value	Language
dc.contributor.author	Li, C https://orcid.org/0000-0001-6600-2664
dc.contributor.author	Xie, H-B
dc.contributor.author	Fan, X
dc.contributor.author	Xu, RYD
dc.contributor.author	Van Huffel, S
dc.contributor.author	Mengersen, K
dc.date.accessioned	2021-02-24T21:02:39Z
dc.date.available	2021-02-24T21:02:39Z
dc.date.issued	2021-01-04
dc.identifier.citation	IEEE transactions on neural networks and learning systems, 2021, 32, (1), pp. 391-404
dc.identifier.issn	2162-237X
dc.identifier.issn	2162-2388
dc.identifier.uri	http://hdl.handle.net/10453/146415
dc.description.abstract	Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensively studied in the machine learning community. Kernelized matrix factorization (KMF) is a powerful tool to incorporate side information into the low-rank approximation model, which has been applied to solve the problems of data mining, recommender systems, image restoration, and machine vision. However, most existing KMF models rely on specifying the rows and columns of the data matrix through a Gaussian process prior and have to tune manually the rank. There are also computational issues of existing models based on regularization or the Markov chain Monte Carlo. In this article, we develop a hierarchical kernelized sparse Bayesian matrix factorization (KSBMF) model to integrate side information. The KSBMF automatically infers the parameters and latent variables including the reduced rank using the variational Bayesian inference. In addition, the model simultaneously achieves low-rankness through sparse Bayesian learning and columnwise sparsity through an enforced constraint on latent factor matrices. We further connect the KSBMF with the nonlocal image processing framework to develop two algorithms for image denoising and inpainting. Experimental results demonstrate that KSBMF outperforms the state-of-the-art approaches for these image-restoration tasks under various levels of corruption.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE transactions on neural networks and learning systems
dc.relation.isbasedon	10.1109/tnnls.2020.2978761
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Kernelized Sparse Bayesian Matrix Factorization.
dc.type	Journal Article
utslib.citation.volume	32
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-24T21:02:32Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	32
utslib.citation.issue	1

Abstract:

Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensively studied in the machine learning community. Kernelized matrix factorization (KMF) is a powerful tool to incorporate side information into the low-rank approximation model, which has been applied to solve the problems of data mining, recommender systems, image restoration, and machine vision. However, most existing KMF models rely on specifying the rows and columns of the data matrix through a Gaussian process prior and have to tune manually the rank. There are also computational issues of existing models based on regularization or the Markov chain Monte Carlo. In this article, we develop a hierarchical kernelized sparse Bayesian matrix factorization (KSBMF) model to integrate side information. The KSBMF automatically infers the parameters and latent variables including the reduced rank using the variational Bayesian inference. In addition, the model simultaneously achieves low-rankness through sparse Bayesian learning and columnwise sparsity through an enforced constraint on latent factor matrices. We further connect the KSBMF with the nonlocal image processing framework to develop two algorithms for image denoising and inpainting. Experimental results demonstrate that KSBMF outperforms the state-of-the-art approaches for these image-restoration tasks under various levels of corruption.

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http://hdl.handle.net/10453/146415