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

Solution path for semi-supervised classification with manifold regularization

Authors Wang, Gang
Chen, Tao
Yeung, Dit-Yan
Lochovsky, Frederick H.
Issue Date 2006
Source IEEE International Conference on Data Mining, 2006, p. 1124-1129
Summary With very low extra computational cost, the entire solution path can be computed for various learning algorithms like support vector classification (SVC) and support vector regression (SVR). In this paper, we extend this promising approach to semi-supervised learning algorithms. In particular, we consider finding the solution path for the Laplacian support vector machine (LapSVM) which is a semi-supervised classification model based on manifold regularization. One advantage of the this algorithm is that the coefficient path is piecewise linear with respect to the regularization parameter, hence its computational complexity is quadratic in the number of labeled examples.
ISSN 1550-4786
ISBN 978-0-7695-2701-7
Rights © 2006 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.
Language English
Format Conference paper
Access View full-text via DOI
View full-text via Web of Science
View full-text via Scopus
Files in this item:
File Description Size Format
yeung.icdm20061.pdf 180938 B Adobe PDF