Multi-instance multi-graph dual embedding learning

Wu, J; Zhu, X; Zhang, C; Cai, Z

Multi-instance multi-graph dual embedding learning

Wu, J

Zhu, X Zhang, C

Cai, Z

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2013, pp. 827 - 836
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Wu, J https://orcid.org/0000-0002-1371-5801	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Cai, Z	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2013, pp. 827 - 836	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27206
dc.description.abstract	Multi-instance learning concerns about building learning models from a number of labeled instance bags, where each bag consists of instances with unknown labels. A bag is labeled positive if one or more multiple instances inside the bag is positive, and negative otherwise. For all existing multi-instance learning algorithms, they are only applicable to the setting where instances in each bag are represented by a set of well defined feature values. In this paper, we advance the problem to a multi-instance multi-graph setting, where a bag contains a number of instances and graphs in pairs, and the learning objective is to derive classification models from labeled bags, containing both instances and graphs, to predict previously unseen bags with maximum accuracy. To achieve the goal, the main challenge is to properly represent graphs inside each bag and further take advantage of complementary information between instance and graph pairs for learning. In the paper, we propose a Dual Embedding Multi-Instance Multi-Graph Learning (DE-MIMG) algorithm, which employs a dual embedding learning approach to (1) embed instance distributions into the informative sub graphs discovery process, and (2) embed discovered sub graphs into the instance feature selection process. The dual embedding process results in an optimal representation for each bag to provide combined instance and graph information for learning. Experiments and comparisons on real-world multi-instance multi-graph learning tasks demonstrate the algorithm performance. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2013.121	en_US
dc.title	Multi-instance multi-graph dual embedding learning	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Dallas, TX, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
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 - ACRI - Australia China Relations Institute
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:

Multi-instance learning concerns about building learning models from a number of labeled instance bags, where each bag consists of instances with unknown labels. A bag is labeled positive if one or more multiple instances inside the bag is positive, and negative otherwise. For all existing multi-instance learning algorithms, they are only applicable to the setting where instances in each bag are represented by a set of well defined feature values. In this paper, we advance the problem to a multi-instance multi-graph setting, where a bag contains a number of instances and graphs in pairs, and the learning objective is to derive classification models from labeled bags, containing both instances and graphs, to predict previously unseen bags with maximum accuracy. To achieve the goal, the main challenge is to properly represent graphs inside each bag and further take advantage of complementary information between instance and graph pairs for learning. In the paper, we propose a Dual Embedding Multi-Instance Multi-Graph Learning (DE-MIMG) algorithm, which employs a dual embedding learning approach to (1) embed instance distributions into the informative sub graphs discovery process, and (2) embed discovered sub graphs into the instance feature selection process. The dual embedding process results in an optimal representation for each bag to provide combined instance and graph information for learning. Experiments and comparisons on real-world multi-instance multi-graph learning tasks demonstrate the algorithm performance. © 2013 IEEE.

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http://hdl.handle.net/10453/27206