Ensemble Manifold Regularization

Geng, B; Tao, D; Xu, C; Yang, L; Hua, X

Ensemble Manifold Regularization

Geng, B Tao, D

Xu, C Yang, L Hua, X

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009, pp. 2396 - 2402
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Geng, B	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Xu, C	en_US
dc.contributor.author	Yang, L	en_US
dc.contributor.author	Hua, X	en_US
dc.contributor.editor	Huttenlocher, D	en_US
dc.contributor.editor	Medioni, G	en_US
dc.contributor.editor	Rehg, J	en_US
dc.date	2009-06-22	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009, pp. 2396 - 2402	en_US
dc.identifier.isbn	978-1-4244-3991-1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17677
dc.description.abstract	We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, pure cross-validation is considered but it does not necessarily scale up. A second problem derives from the suboptimality incurred by discrete grid search and overfitting problems. As a consequence, we developed an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR very carefully so that it (a) learns both the composite manifold and the semi-supervised classifier jointly; (b) is fully automatic for learning the intrinsic manifold hyperparameters implicitly; (c) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption; and (d) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Extensive experiments over both synthetic and real datasets show the effectiveness of the proposed framework.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	IEEE Computer Society Conference on Computer Vision and Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/CVPR.2009.5206695	en_US
dc.rights	© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Ensemble Manifold Regularization	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Miami USA	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Miami USA	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.consider-herdc	false	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2009-06-22	en_US

Abstract:

We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, pure cross-validation is considered but it does not necessarily scale up. A second problem derives from the suboptimality incurred by discrete grid search and overfitting problems. As a consequence, we developed an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR very carefully so that it (a) learns both the composite manifold and the semi-supervised classifier jointly; (b) is fully automatic for learning the intrinsic manifold hyperparameters implicitly; (c) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption; and (d) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Extensive experiments over both synthetic and real datasets show the effectiveness of the proposed framework.

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http://hdl.handle.net/10453/17677