Multi-Task Learning for Blind Source Separation

Du, B; Wang, S; Xu, C; Wang, N; Zhang, L; Tao, D

Multi-Task Learning for Blind Source Separation

Du, B Wang, S Xu, C

Wang, N Zhang, L Tao, D

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2018, 27 (9), pp. 4219 - 4231
Issue Date:: 2018-09-01

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Field	Value	Language
dc.contributor.author	Du, B	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Xu, C https://orcid.org/0000-0002-4756-0609	en_US
dc.contributor.author	Wang, N	en_US
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2018-09-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2018, 27 (9), pp. 4219 - 4231	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132252
dc.description.abstract	© 1992-2012 IEEE. Blind source separation (BSS) aims to discover the underlying source signals from a set of linear mixture signals without any prior information of the mixing system, which is a fundamental problem in signal and image processing field. Most of the state-of-the-art algorithms have independently handled the decompositions of mixture signals. In this paper, we propose a new algorithm named multi-task sparse model to solve the BSS problem. Source signals are characterized via sparse techniques. Meanwhile, we regard the decomposition of each mixture signal as a task and employ the idea of multi-task learning to discover connections between tasks for the accuracy improvement of the source signal separation. Theoretical analyses on the optimization convergence and sample complexity of the proposed algorithm are provided. Experimental results based on extensive synthetic and real-world data demonstrate the necessity of exploiting connections between mixture signals and the effectiveness of the proposed algorithm.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2018.2836324	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Multi-Task Learning for Blind Source Separation	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	27	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	27	en_US

Abstract:

© 1992-2012 IEEE. Blind source separation (BSS) aims to discover the underlying source signals from a set of linear mixture signals without any prior information of the mixing system, which is a fundamental problem in signal and image processing field. Most of the state-of-the-art algorithms have independently handled the decompositions of mixture signals. In this paper, we propose a new algorithm named multi-task sparse model to solve the BSS problem. Source signals are characterized via sparse techniques. Meanwhile, we regard the decomposition of each mixture signal as a task and employ the idea of multi-task learning to discover connections between tasks for the accuracy improvement of the source signal separation. Theoretical analyses on the optimization convergence and sample complexity of the proposed algorithm are provided. Experimental results based on extensive synthetic and real-world data demonstrate the necessity of exploiting connections between mixture signals and the effectiveness of the proposed algorithm.

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http://hdl.handle.net/10453/132252