Learning Robust Low-Rank Approximation for Crowdsourcing on Riemannian Manifold

Li, Q; Wang, Z; Li, G; Cao, Y; Xiong, G; Guo, L

Learning Robust Low-Rank Approximation for Crowdsourcing on Riemannian Manifold

Li, Q

Wang, Z Li, G Cao, Y Xiong, G Guo, L

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Procedia Computer Science, 2017, 108C, pp. 285-294
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Li, Q https://orcid.org/0000-0002-8308-9551
dc.contributor.author	Wang, Z
dc.contributor.author	Li, G
dc.contributor.author	Cao, Y
dc.contributor.author	Xiong, G
dc.contributor.author	Guo, L
dc.contributor.editor	Koumoutsakos, P
dc.contributor.editor	Lees, M
dc.contributor.editor	Krzhizhanovskaya, V
dc.contributor.editor	Dongarra, J
dc.contributor.editor	Sloot, P
dc.date.accessioned	2022-09-14T03:34:29Z
dc.date.available	2022-09-14T03:34:29Z
dc.date.issued	2017-01-01
dc.identifier.citation	Procedia Computer Science, 2017, 108C, pp. 285-294
dc.identifier.issn	1877-0509
dc.identifier.issn	1877-0509
dc.identifier.uri	http://hdl.handle.net/10453/161815
dc.description.abstract	Recently, crowdsourcing has attracted substantial research interest due to its efficiency in collecting labels for machine learning and computer vision tasks. This paper proposes a Rieman-nian manifold optimization algorithm, ROLA (Robust Low-rank Approximation), to aggregate the labels from a novel perspective. Specifically, a novel low-rank approximation model is proposed to capture underlying correlation among annotators meanwhile identify annotator-specific noise. More significantly, ROLA defines the label noise in crowdsourcing as annotator-specific noise, which can be well regularized by l2,1-norm. The proposed ROLA can improve the aggregation performance when compared with state-of-the-art crowdsourcing methods.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Procedia Computer Science
dc.relation.isbasedon	10.1016/j.procs.2017.05.179
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 10 Technology
dc.title	Learning Robust Low-Rank Approximation for Crowdsourcing on Riemannian Manifold
dc.type	Journal Article
utslib.citation.volume	108C
utslib.location.activity	Zurich, SWITZERLAND
utslib.for	08 Information and Computing Sciences
utslib.for	10 Technology
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 - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-09-14T03:34:28Z
pubs.publication-status	Published
pubs.volume	108C

Abstract:

Recently, crowdsourcing has attracted substantial research interest due to its efficiency in collecting labels for machine learning and computer vision tasks. This paper proposes a Rieman-nian manifold optimization algorithm, ROLA (Robust Low-rank Approximation), to aggregate the labels from a novel perspective. Specifically, a novel low-rank approximation model is proposed to capture underlying correlation among annotators meanwhile identify annotator-specific noise. More significantly, ROLA defines the label noise in crowdsourcing as annotator-specific noise, which can be well regularized by l2,1-norm. The proposed ROLA can improve the aggregation performance when compared with state-of-the-art crowdsourcing methods.

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http://hdl.handle.net/10453/161815