Semi-Supervised Learning -- A Statistical Physics Approach
摘要
We present a novel approach to semi-supervised learning which is based on statistical physics. Most of the former work in the field of semi-supervised learning classifies the points by minimizing a certain energy function, which corresponds to a minimal k-way cut solution. In contrast to these methods, we estimate the distribution of classifications, instead of the sole minimal k-way cut, which yields more accurate and robust results. Our approach may be applied to all energy functions used for semi-supervised learning. The method is based on sampling using a Multicanonical Markov chain Monte-Carlo algorithm, and has a straightforward probabilistic interpretation, which allows for soft assignments of points to classes, and also to cope with yet unseen class types. The suggested approach is demonstrated on a toy data set and on two real-life data sets of gene expression.
引用
@article{arxiv.cs/0604011,
title = {Semi-Supervised Learning -- A Statistical Physics Approach},
author = {Gad Getz and Noam Shental and Eytan Domany},
journal= {arXiv preprint arXiv:cs/0604011},
year = {2007}
}
备注
9 pages. Appeared in in the Proceedings of "Learning with Partially Classified Training Data", ICML 2005 workshop