Nonparametric bayesian nonnegative matrix factorization

Xie, HB; Li, C; Mengersen, K; Wang, S; Xu, RYD

Nonparametric bayesian nonnegative matrix factorization

Xie, HB Li, C

Mengersen, K Wang, S Xu, RYD

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Modeling Decisions for Artificial Intelligence, 2020, 12256, pp. 132-141
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Xie, HB
dc.contributor.author	Li, C https://orcid.org/0000-0001-6600-2664
dc.contributor.author	Mengersen, K
dc.contributor.author	Wang, S
dc.contributor.author	Xu, RYD
dc.date	2020-09-02
dc.date.accessioned	2021-06-07T01:49:29Z
dc.date.available	2021-06-07T01:49:29Z
dc.date.issued	2020-01-01
dc.identifier.citation	Modeling Decisions for Artificial Intelligence, 2020, 12256, pp. 132-141
dc.identifier.isbn	9783030575236
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/149435
dc.description.abstract	© Springer Nature Switzerland AG 2020. Nonnegative Matrix Factorization (NMF) is an important tool in machine learning for blind source separation and latent factor extraction. Most of existing NMF algorithms assume a specific noise kernel, which is insufficient to deal with complex noise in real scenarios. In this study, we present a hierarchical nonparametric nonnegative matrix factorization (NPNMF) model in which the Gaussian mixture model is used to approximate the complex noise distribution. The model is cast in the nonparametric Bayesian framework by using Dirichlet process mixture to infer the necessary number of Gaussian components. We derive a mean-field variational inference algorithm for the proposed nonparametric Bayesian model. Experimental results on both synthetic data and electroencephalogram (EEG) demonstrate that NPNMF performs better in extracting the latent nonnegative factors in comparison with state-of-the-art methods.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Modeling Decisions for Artificial Intelligence
dc.relation.ispartof	International Conference on Modeling Decisions for Artificial Intelligence
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-3-030-57524-3_11
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Nonparametric bayesian nonnegative matrix factorization
dc.type	Conference Proceeding
utslib.citation.volume	12256
utslib.location.activity	Sant Cugat, Spain
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
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	*
pubs.consider-herdc	false
dc.date.updated	2021-06-07T01:49:28Z
pubs.finish-date	2020-09-04
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2020-09-02
pubs.volume	12256
dc.location	Switzerland

Abstract:

© Springer Nature Switzerland AG 2020. Nonnegative Matrix Factorization (NMF) is an important tool in machine learning for blind source separation and latent factor extraction. Most of existing NMF algorithms assume a specific noise kernel, which is insufficient to deal with complex noise in real scenarios. In this study, we present a hierarchical nonparametric nonnegative matrix factorization (NPNMF) model in which the Gaussian mixture model is used to approximate the complex noise distribution. The model is cast in the nonparametric Bayesian framework by using Dirichlet process mixture to infer the necessary number of Gaussian components. We derive a mean-field variational inference algorithm for the proposed nonparametric Bayesian model. Experimental results on both synthetic data and electroencephalogram (EEG) demonstrate that NPNMF performs better in extracting the latent nonnegative factors in comparison with state-of-the-art methods.

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http://hdl.handle.net/10453/149435