A Brief Review of Spin-Glass Applications in Unsupervised and Semi-supervised Learning

Spin-glass theory developed in statistical mechanics has found its usage in various information science problems. In this study, we focus on the application of spin-glass models in unsupervised and semi-supervised learning. Several key papers in this field are reviewed, to answer the question that why and how spin-glass is adopted. The question can be answered from two aspects.

[1]  Eytan Domany,et al.  Semi-Supervised Learning -- A Statistical Physics Approach , 2006, ArXiv.

[2]  C. Fortuin,et al.  On the random-cluster model: I. Introduction and relation to other models , 1972 .

[3]  Shao-Meng Qin,et al.  Spin-glass phase transitions and minimum energy of the random feedback vertex set problem. , 2016, Physical review. E.

[4]  H. Nishimori.  Statistical Physics of Spin Glasses and Information Processing , 2001 .

[5]  R. Swendsen,et al.  Cluster Monte Carlo algorithms , 1990 .

[6]  Berg,et al.  Multicanonical ensemble: A new approach to simulate first-order phase transitions. , 1992, Physical review letters.

[7]  Eric Eaton,et al.  A Spin-Glass Model for Semi-Supervised Community Detection , 2012, AAAI.

[8]  Eytan Domany,et al.  Superparamagnetic Clustering of Data , 1996 .

[9]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[10]  Eytan Domany,et al.  Data Clustering Using a Model Granular Magnet , 1997, Neural Computation.

[11]  James P. Sethna,et al.  Effective Hamiltonians of 2D Spin Glass Clusters , 2015 .

[12]  F. Y. Wu.  The Potts model , 1982 .

[13]  J. Reichardt,et al.  Statistical mechanics of community detection. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  J. Inoue,et al.  Image restoration using the Q-Ising spin glass. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Sompolinsky,et al.  Storing infinite numbers of patterns in a spin-glass model of neural networks. , 1985, Physical review letters.

[16]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Nicolas Sourlas,et al.  Spin-glass models as error-correcting codes , 1989, Nature.