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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, Hong Kong, China, 18-22 December 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
Language English
Format Conference paper
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