Semi-Supervised Learning -- A Statistical Physics Approach

Gad Getz; Noam Shental; Eytan Domany

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Semi-Supervised Learning -- A Statistical Physics Approach

Gad Getz·Noam Shental·Eytan Domany

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Abstract

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.

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Gad Getz, Noam Shental, Eytan Domany. 2006-04-06. Semi-Supervised Learning -- A Statistical Physics Approach. https://arxiv.org/abs/cs/0604011

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