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. Firstly, adopting spin-glass models enables the vast knowledge base developed in statistical mechanics to be used, such as the self-organizing grains at the superparamagnetic phase has a natural connection to clustering. Secondly, spin-glass model can serve as a bridge for model development, i.e., one can map existing model into spin-glass manner, facilitate it with new features and finally map it back.
展开▼
