nips nips2005 nips2005-27 nips2005-27-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Tong Zhang, Rie Kubota Ando
Abstract: We consider a framework for semi-supervised learning using spectral decomposition based un-supervised kernel design. This approach subsumes a class of previously proposed semi-supervised learning methods on data graphs. We examine various theoretical properties of such methods. In particular, we derive a generalization performance bound, and obtain the optimal kernel design by minimizing the bound. Based on the theoretical analysis, we are able to demonstrate why spectral kernel design based methods can often improve the predictive performance. Experiments are used to illustrate the main consequences of our analysis.
[1] Mikhail Belkin and Partha Niyogi. Semi-supervised learning on Riemannian manifolds. Machine Learning, Special Issue on Clustering:209–239, 2004.
[2] Olivier Chapelle, Jason Weston, and Bernhard Sch:olkopf. Cluster kernels for semisupervised learning. In NIPS, 2003.
[3] Andrew Y. Ng, Michael I. Jordan, and Yair Weiss. On spectral clustering: Analysis and an algorithm. In NIPS, pages 849–856, 2001.
[4] M. Szummer and T. Jaakkola. Partially labeled classification with Markov random walks. In NIPS 2001, 2002.
[5] D. Zhou, O. Bousquet, T.N. Lal, J. Weston, and B. Schlkopf. Learning with local and global consistency. In NIPS 2003, pages 321–328, 2004.
[6] Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty. Semi-supervised learning using Gaussian fields and harmonic functions. In ICML 2003, 2003.