Online Semi-Supervised Multi-Task Distance Metric Learning

Li, Y; Tao, D

Online Semi-Supervised Multi-Task Distance Metric Learning

Li, Y Tao, D

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Data Mining Workshops, ICDMW, 2016, 0 pp. 474 - 479
Issue Date:: 2016-07-02

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Field	Value	Language
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2016-07-02	en_US
dc.identifier.citation	IEEE International Conference on Data Mining Workshops, ICDMW, 2016, 0 pp. 474 - 479	en_US
dc.identifier.isbn	9781509054725	en_US
dc.identifier.issn	2375-9232	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127124
dc.description.abstract	© 2016 IEEE. Given several related tasks, multi-Task learning can improve the performance of each task through sharing parameters or feature representations. In this paper, we apply multi-Task learning to a particular case of distance metric learning, in which we have a small amount of labeled data. Consider the effectiveness of semi-supervised learning handling few labeled machine learning problems, we integrate semi-supervised learning with multi-Task learning and distance metric learning. One of the defect of multi-Task learning is its low training efficiency, as we need all the training examples from all tasks to train a model. We propose an online learning algorithm to overcome this drawback of multi-Task learning. Experiments are conducted on one landmark multi-Task learning dataset to demonstrate the efficiency and effectiveness of our online semi-supervised multi-Task learning algorithm.	en_US
dc.relation.ispartof	IEEE International Conference on Data Mining Workshops, ICDMW	en_US
dc.relation.isbasedon	10.1109/ICDMW.2016.0074	en_US
dc.title	Online Semi-Supervised Multi-Task Distance Metric Learning	en_US
dc.type	Conference Proceeding
utslib.citation.volume	0	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	0	en_US

Abstract:

© 2016 IEEE. Given several related tasks, multi-Task learning can improve the performance of each task through sharing parameters or feature representations. In this paper, we apply multi-Task learning to a particular case of distance metric learning, in which we have a small amount of labeled data. Consider the effectiveness of semi-supervised learning handling few labeled machine learning problems, we integrate semi-supervised learning with multi-Task learning and distance metric learning. One of the defect of multi-Task learning is its low training efficiency, as we need all the training examples from all tasks to train a model. We propose an online learning algorithm to overcome this drawback of multi-Task learning. Experiments are conducted on one landmark multi-Task learning dataset to demonstrate the efficiency and effectiveness of our online semi-supervised multi-Task learning algorithm.

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http://hdl.handle.net/10453/127124