Multi-view positive and unlabeled learning

Zhou, JT; Pan, SJ; Mao, Q; Tsang, IW

Multi-view positive and unlabeled learning

Zhou, JT Pan, SJ Mao, Q Tsang, IW

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Publication Type:: Journal Article
Citation:: Journal of Machine Learning Research, 2012, 25 pp. 555 - 570
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Zhou, JT	en_US
dc.contributor.author	Pan, SJ	en_US
dc.contributor.author	Mao, Q	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Journal of Machine Learning Research, 2012, 25 pp. 555 - 570	en_US
dc.identifier.issn	1532-4435	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121428
dc.description.abstract	Learning with Positive and Unlabeled instances (PU learning) arises widely in information retrieval applications. To address the unavailability issue of negative instances, most existing PU learning approaches require to either identify a reliable set of negative instances from the unlabeled data or estimate probability densities as an intermediate step. However, inaccurate negative-instance identification or poor density estimation may severely degrade overall performance of the final predictive model. To this end, we propose a novel PU learning method based on density ratio estimation without constructing any sets of negative instances or estimating any intermediate densities. To further boost PU learning performance, we extend our proposed learning method in a multi-view manner by utilizing multiple heterogeneous sources. Extensive experimental studies demonstrate the effectiveness of our proposed methods, especially when positive labeled data are limited. © 2012 J.T. Zhou, S.J. Pan, Q. Mao & I.W. Tsang.	en_US
dc.relation.ispartof	Journal of Machine Learning Research	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Multi-view positive and unlabeled learning	en_US
dc.type	Journal Article
utslib.citation.volume	25	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	17 Psychology and 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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

Learning with Positive and Unlabeled instances (PU learning) arises widely in information retrieval applications. To address the unavailability issue of negative instances, most existing PU learning approaches require to either identify a reliable set of negative instances from the unlabeled data or estimate probability densities as an intermediate step. However, inaccurate negative-instance identification or poor density estimation may severely degrade overall performance of the final predictive model. To this end, we propose a novel PU learning method based on density ratio estimation without constructing any sets of negative instances or estimating any intermediate densities. To further boost PU learning performance, we extend our proposed learning method in a multi-view manner by utilizing multiple heterogeneous sources. Extensive experimental studies demonstrate the effectiveness of our proposed methods, especially when positive labeled data are limited. © 2012 J.T. Zhou, S.J. Pan, Q. Mao & I.W. Tsang.

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http://hdl.handle.net/10453/121428