A semi-supervised framework for feature mapping and multiclass classification

Chen, B; Lam, W; Tsang, I; Wong, TL

A semi-supervised framework for feature mapping and multiclass classification

Chen, B Lam, W Tsang, I

Wong, TL

Permalink

Publication Type:: Conference Proceeding
Citation:: Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics, 2009, 1 pp. 337 - 348
Issue Date:: 2009-12-01

Closed Access

	Filename	Description	Size
	2013006913OK.pdf	Published version	1.35 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Chen, B	en_US
dc.contributor.author	Lam, W	en_US
dc.contributor.author	Tsang, I https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Wong, TL	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics, 2009, 1 pp. 337 - 348	en_US
dc.identifier.isbn	9781615671090	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32926
dc.description.abstract	We propose a semi-supervised framework incorporating feature mapping with multiclass classification. By learning multiple classification tasks simultaneously, this framework can learn the latent feature space effectively for both labeled and unlabeled data. The knowledge in the transformed space can be transferred not only between the labeled and unlabeled data, but also across multiple classes, so as to improve the classification performance given a small amount of labeled data. We show that this problem is equivalent to a sequential convex optimization problem by applying constraint concave-convex procedure (CCCP). Efficient algorithm with theoretical guarantee is proposed and computational issue is investigated. Extensive experiments have been conducted to demonstrate the effectiveness of our proposed framework.	en_US
dc.relation.ispartof	Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics	en_US
dc.title	A semi-supervised framework for feature mapping and multiclass classification	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1	en_US
utslib.for	0804 Data Format	en_US
dc.location.activity	Sparks, Nevada
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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	1	en_US

Abstract:

We propose a semi-supervised framework incorporating feature mapping with multiclass classification. By learning multiple classification tasks simultaneously, this framework can learn the latent feature space effectively for both labeled and unlabeled data. The knowledge in the transformed space can be transferred not only between the labeled and unlabeled data, but also across multiple classes, so as to improve the classification performance given a small amount of labeled data. We show that this problem is equivalent to a sequential convex optimization problem by applying constraint concave-convex procedure (CCCP). Efficient algorithm with theoretical guarantee is proposed and computational issue is investigated. Extensive experiments have been conducted to demonstrate the effectiveness of our proposed framework.

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

http://hdl.handle.net/10453/32926