Transductive Component Analysis
Refereed conference paper presented and published in conference proceedings

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AbstractIn this paper, we study semi-supervised linear dimensionality reduction. Beyond conventional supervised methods which merely consider labeled instances, the semi-supervised 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. Specific-ally, we present the Transductive Component Analysis (TCA) algorithm to generate such a subspace founded on a graph-theoretic framework. Considering TCA is non-orthogonal, 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.
All Author(s) ListLiu W, Tao DC, Liu JZ
Name of Conference8th IEEE International Conference on Data Mining
Start Date of Conference15/12/2008
End Date of Conference19/12/2008
Place of ConferencePisa
Country/Region of ConferenceItaly
Pages433 - 442
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering; Engineering, Electrical & Electronic

Last updated on 2020-28-11 at 00:50