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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/7467
Title: Solution path for semi-supervised classification with manifold regulation
Authors: Wang, Gang
Yeung, Dit-Yan
Lochovsky, Frederick H.
Keywords: Semi-supervised classification
Manifolds
Supervised learning
Issue Date: Dec-2006
Citation: Proceedings of the 6th International Conference on Data mining, ICDM 2006, 18-22 December, 2006, Hong Kong SAR, China, p. 1124-1129
Abstract: 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.
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.
URI: http://hdl.handle.net/1783.1/7467
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