Semi-Supervised Learning -- A Statistical Physics Approach

نویسندگان

  • Gad Getz
  • Noam Shental
  • Eytan Domany
چکیده

We present a novel approach to semisupervised 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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عنوان ژورنال:
  • CoRR

دوره abs/cs/0604011  شماره 

صفحات  -

تاریخ انتشار 2004