Semi-Supervised Learning -- A Statistical Physics Approach
<|reference_start|>Semi-Supervised Learning -- A Statistical Physics Approach: 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.<|reference_end|>
@article{getz2006semi-supervised,
    title={Semi-Supervised Learning -- A Statistical Physics Approach},
    author={Gad Getz, Noam Shental, Eytan Domany},
    journal={arXiv preprint arXiv:cs/0604011},
    year={2006},
    archivePrefix={arXiv},
    eprint={cs/0604011},
    primaryClass={cs.LG cond-mat.stat-mech cs.CV}
}