Direct discriminative bag mapping for multi-instance learning

Wu, J; Pan, S; Zhang, P; Zhu, X

Direct discriminative bag mapping for multi-instance learning

Wu, J

Pan, S

Zhang, P

Zhu, X

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Publication Type:: Conference Proceeding
Citation:: 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, pp. 4274 - 4275
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Wu, J https://orcid.org/0000-0002-1371-5801	en_US
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Zhu, X	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, pp. 4274 - 4275	en_US
dc.identifier.isbn	9781577357605	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121838
dc.description.abstract	© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Multi-instance learning (MIL) is useful for tackling labeling ambiguity in learning tasks, by allowing a bag of instances to share one label. Recently, bag mapping methods, which transform a bag to a single instance in a new space via instance selection, have drawn significant attentions. To date, most existing works are developed based on the original space, i.e., utilizing all instances for bag mapping, and instance selection is indirectly tied to the MIL objective. As a result, it is hard to guarantee the distinguish capacity of the selected instances in the new bag mapping space for MIL. In this paper, we propose a direct discriminative mapping approach for multi-instance learning (MILDM), which identifies instances to directly distinguish bags in the new mapping space. Experiments and comparisons on real-world learning tasks demonstrate the algorithm performance.	en_US
dc.relation.ispartof	30th AAAI Conference on Artificial Intelligence, AAAI 2016	en_US
dc.title	Direct discriminative bag mapping for multi-instance learning	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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 - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Multi-instance learning (MIL) is useful for tackling labeling ambiguity in learning tasks, by allowing a bag of instances to share one label. Recently, bag mapping methods, which transform a bag to a single instance in a new space via instance selection, have drawn significant attentions. To date, most existing works are developed based on the original space, i.e., utilizing all instances for bag mapping, and instance selection is indirectly tied to the MIL objective. As a result, it is hard to guarantee the distinguish capacity of the selected instances in the new bag mapping space for MIL. In this paper, we propose a direct discriminative mapping approach for multi-instance learning (MILDM), which identifies instances to directly distinguish bags in the new mapping space. Experiments and comparisons on real-world learning tasks demonstrate the algorithm performance.

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http://hdl.handle.net/10453/121838