Transductive component analysis

Liu, W; Tao, D; Liu, J

Transductive component analysis

Liu, W Tao, D

Liu, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2008, pp. 433 - 442
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Liu, W	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Liu, J	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2008, pp. 433 - 442	en_US
dc.identifier.isbn	9780769535029	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17294
dc.description.abstract	In this paper, we study semi-supervised linear dimensionality reduction. Beyond conventional supervised methods which merely consider labeled instances, the semisupervised scheme allows to leverage abundant and ample unlabeled instances into learning so as to achieve better generalization performance. Under semi-supervised settings, our objective is to learn a smooth as well as discriminative subspace and linear dimensionality reduction is thus achieved by mapping all samples into the subspace. Specifically, we present the Transductive Component Analysis (TCA) algorithm to generate such a subspace founded on a graph-theoretic framework. Considering TCA is nonorthogonal, we further present the Orthogonal Transductive Component Analysis (OTCA) algorithm to iteratively produce a series of orthogonal basis vectors. OTCA has better discriminating power than TCA. Experiments carried out on synthetic and real-world datasets by OTCA show a clear improvement over the results of representative dimensionality reduction algorithms. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2008.101	en_US
dc.title	Transductive component analysis	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Pisa, Italy	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:

In this paper, we study semi-supervised linear dimensionality reduction. Beyond conventional supervised methods which merely consider labeled instances, the semisupervised scheme allows to leverage abundant and ample unlabeled instances into learning so as to achieve better generalization performance. Under semi-supervised settings, our objective is to learn a smooth as well as discriminative subspace and linear dimensionality reduction is thus achieved by mapping all samples into the subspace. Specifically, we present the Transductive Component Analysis (TCA) algorithm to generate such a subspace founded on a graph-theoretic framework. Considering TCA is nonorthogonal, we further present the Orthogonal Transductive Component Analysis (OTCA) algorithm to iteratively produce a series of orthogonal basis vectors. OTCA has better discriminating power than TCA. Experiments carried out on synthetic and real-world datasets by OTCA show a clear improvement over the results of representative dimensionality reduction algorithms. © 2008 IEEE.

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http://hdl.handle.net/10453/17294