Compact multiple-instance learning

Chai, J; Liu, W; Tsang, IW; Shen, X

Compact multiple-instance learning

Chai, J Liu, W Tsang, IW

Shen, X

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Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2017, Part F131841 pp. 2007 - 2010
Issue Date:: 2017-11-06

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Field	Value	Language
dc.contributor.author	Chai, J	en_US
dc.contributor.author	Liu, W	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Shen, X https://orcid.org/0000-0001-8494-4532	en_US
dc.date.issued	2017-11-06	en_US
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2017, Part F131841 pp. 2007 - 2010	en_US
dc.identifier.isbn	9781450349185	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126785
dc.description.abstract	© 2017 Association for Computing Machinery. The weakly supervised Multiple-Instance Learning (MIL) problem has been successfully applied in information retrieval tasks. Two related issues might affect the performance of MIL algorithms: how to cope with label ambiguities and how to deal with non-discriminative components, and we propose COmpact MultiPle-Instance LEarning (COMPILE) to consider them simultaneously. To treat label ambiguities, COMPILE seeks ground-truth positive instances in positive bags. By using weakly supervised information to learn data's short binary representations, COMPILE enhances discrimination via strengthening discriminative components and suppressing non-discriminative ones. We adapt block coordinate descent to optimize COMPILE efficiently. Experiments on text categorization empirically show: 1) COMPILE unifies disambiguation and data preprocessing successfully; 2) it generates short binary representations efficiently to enhance discrimination at significantly reduced storage cost.	en_US
dc.relation	http://purl.org/au-research/grants/arc/FT130100746
dc.relation	http://purl.org/au-research/grants/arc/LP150100671
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings	en_US
dc.relation.isbasedon	10.1145/3132847.3133070	en_US
dc.title	Compact multiple-instance learning	en_US
dc.type	Conference Proceeding
utslib.citation.volume	Part F131841	en_US
utslib.for	0806 Information Systems	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	Part F131841	en_US

Abstract:

© 2017 Association for Computing Machinery. The weakly supervised Multiple-Instance Learning (MIL) problem has been successfully applied in information retrieval tasks. Two related issues might affect the performance of MIL algorithms: how to cope with label ambiguities and how to deal with non-discriminative components, and we propose COmpact MultiPle-Instance LEarning (COMPILE) to consider them simultaneously. To treat label ambiguities, COMPILE seeks ground-truth positive instances in positive bags. By using weakly supervised information to learn data's short binary representations, COMPILE enhances discrimination via strengthening discriminative components and suppressing non-discriminative ones. We adapt block coordinate descent to optimize COMPILE efficiently. Experiments on text categorization empirically show: 1) COMPILE unifies disambiguation and data preprocessing successfully; 2) it generates short binary representations efficiently to enhance discrimination at significantly reduced storage cost.

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http://hdl.handle.net/10453/126785