Sublinear Computational Time Modeling by Momentum-Space Renormalization Group Theory in Statistical Machine Learning Procedures

Kazuyuki Tanaka (), Masayuki Ohzeki () and Muneki Yasuda ()
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Kazuyuki Tanaka: Tohoku University
Masayuki Ohzeki: Tohoku University
Muneki Yasuda: Yamagata University

The Review of Socionetwork Strategies, 2019, vol. 13, issue 2, 281-306

Abstract: Abstract We review sublinear computational time modeling using momentum-space renormalization group approaches in the statistical machine learning algorithms. The modeling scheme has been proposed and the basic frameworks have been briefly explained in a short note (Tanaka et al. in J. Phys. Soc. Jpn, 87(8), Article ID: 085001, 2018). We present their detailed formulations and some numerical experimental results of sublinear computational time modeling based on the momentum-space renormalization scheme.

Keywords: Statistical machine learning; Bayesian modeling; Probabilistic graphical model; Renormalization group; Statistical mechanical informatics (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s12626-019-00053-1

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