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

Solution path for semi-supervised classification with manifold regularization

Authors Wang, Gang HKUST affiliated (currently or previously)
Chen, Tao HKUST affiliated (currently or previously)
Yeung, Dit-Yan View this author's profile
Lochovsky, Frederick H. View this author's profile
Issue Date 2006
Source Proceedings - IEEE International Conference on Data Mining, 6th International Conference on Data Mining , 2006, p. 1124-1129, Article number 4053165
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
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