Regularized large margin distance metric learning

Li, Y; Tian, X; Tao, D

Regularized large margin distance metric learning

Li, Y Tian, X Tao, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2017, pp. 1015 - 1022
Issue Date:: 2017-01-31

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Field	Value	Language
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Tian, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2017-01-31	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2017, pp. 1015 - 1022	en_US
dc.identifier.isbn	9781509054725	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127122
dc.description.abstract	© 2016 IEEE. Distance metric learning plays an important role in many applications, such as classification and clustering. In this paper, we propose a novel distance metric learning using two hinge losses in the objective function. One is the constraint of the pairs which makes the similar pairs (the same label) closer and the dissimilar (different labels) pairs separated as far as possible. The other one is the constraint of the triplets which makes the largest distance between pairs intra the class larger than the smallest distance between pairs inter the classes. Previous works only consider one of the two kinds of constraints. Additionally, different from the triplets used in previous works, we just need a small amount of such special triplets. This improves the efficiency of our proposed method. Consider the situation in which we might not have enough labeled samples, we extend the proposed distance metric learning into a semi-supervised learning framework. Experiments are conducted on several landmark datasets and the results demonstrate the effectiveness of our proposed method.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2016.133	en_US
dc.title	Regularized large margin distance metric learning	en_US
dc.type	Conference Proceeding
utslib.for	0802 Computation Theory and Mathematics	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

Abstract:

© 2016 IEEE. Distance metric learning plays an important role in many applications, such as classification and clustering. In this paper, we propose a novel distance metric learning using two hinge losses in the objective function. One is the constraint of the pairs which makes the similar pairs (the same label) closer and the dissimilar (different labels) pairs separated as far as possible. The other one is the constraint of the triplets which makes the largest distance between pairs intra the class larger than the smallest distance between pairs inter the classes. Previous works only consider one of the two kinds of constraints. Additionally, different from the triplets used in previous works, we just need a small amount of such special triplets. This improves the efficiency of our proposed method. Consider the situation in which we might not have enough labeled samples, we extend the proposed distance metric learning into a semi-supervised learning framework. Experiments are conducted on several landmark datasets and the results demonstrate the effectiveness of our proposed method.

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http://hdl.handle.net/10453/127122