Abstract
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.
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Zhu, L., Ikeda, K., Pang, P., Zhang, R., Sarrafzadeh, A. (2016). A Brief Review of Spin-Glass Applications in Unsupervised and Semi-supervised Learning. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_64
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DOI: https://doi.org/10.1007/978-3-319-46687-3_64
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