Large-margin weakly supervised dimensionality reduction

Xu, C; Tao, D; Rui, Y

Large-margin weakly supervised dimensionality reduction

Xu, C

Tao, D

Rui, Y

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Publication Type:: Conference Proceeding
Citation:: 31st International Conference on Machine Learning, ICML 2014, 2014, 3 pp. 2472 - 2482
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Xu, C https://orcid.org/0000-0002-4756-0609	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Rui, Y	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	31st International Conference on Machine Learning, ICML 2014, 2014, 3 pp. 2472 - 2482	en_US
dc.identifier.isbn	9781634393973	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118898
dc.description.abstract	Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. This paper studies dimensionality reduction in a weakly supervised setting, in which the preference relationship between examples is indicated by weak cues. A novel framework is proposed that integrates two aspects of the large margin principle (angle and distance), which simultaneously encourage angle consistency between preference pairs and maximize the distance between examples in preference pairs. Two specific algorithms are developed: an alternating direction method to learn a linear transformation matrix and a gradient boosting technique to optimize a non-linear transformation directly in the function space. Theoretical analysis demonstrates that the proposed large margin optimization criteria can strengthen and improve the robustness and generalization performance of preference learning algorithms on the obtained low-dimensional subspace. Experimental results on real-world datasets demonstrate the significance of studying dimensionality reduction in the weakly supervised setting and the effectiveness of the proposed framework.	en_US
dc.relation.ispartof	31st International Conference on Machine Learning, ICML 2014	en_US
dc.title	Large-margin weakly supervised dimensionality reduction	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3	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	open_access
pubs.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. This paper studies dimensionality reduction in a weakly supervised setting, in which the preference relationship between examples is indicated by weak cues. A novel framework is proposed that integrates two aspects of the large margin principle (angle and distance), which simultaneously encourage angle consistency between preference pairs and maximize the distance between examples in preference pairs. Two specific algorithms are developed: an alternating direction method to learn a linear transformation matrix and a gradient boosting technique to optimize a non-linear transformation directly in the function space. Theoretical analysis demonstrates that the proposed large margin optimization criteria can strengthen and improve the robustness and generalization performance of preference learning algorithms on the obtained low-dimensional subspace. Experimental results on real-world datasets demonstrate the significance of studying dimensionality reduction in the weakly supervised setting and the effectiveness of the proposed framework.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/118896