Quantum annealing for Dirichlet process mixture models with applications to network clustering

Issei Sato*, Shu Tanaka, Kenichi Kurihara, Seiji Miyashita, Hiroshi Nakagawa

*この研究の対応する著者

研究成果: Article査読

12 被引用数 (Scopus)

抄録

We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP). QA is a parallelized extension of simulated annealing (SA), i.e., it is a parallel stochastic optimization technique. Existing approaches ( Kurihara et al. 2009 [12] and Sato et al. 2009 [20]) cannot be applied to the CRP because their QA framework is formulated using a fixed number of mixture components. The proposed QA algorithm can handle an unfixed number of classes in mixture models. We applied QA to a DPM model for clustering vertices in a network where a CRP seating arrangement indicates a network partition. A multi core processer was used for running QA in experiments, the results of which show that QA is better than SA, Markov chain Monte Carlo inference, and beam search at finding a maximum a posteriori estimation of a seating arrangement in the CRP. Since our QA algorithm is as easy as to implement the SA algorithm, it is suitable for a wide range of applications.

本文言語English
ページ(範囲)523-531
ページ数9
ジャーナルNeurocomputing
121
DOI
出版ステータスPublished - 2013 12月 9
外部発表はい

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • 認知神経科学

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