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

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

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)523-531
Number of pages9
JournalNeurocomputing
Volume121
DOIs
Publication statusPublished - 2013 Dec 9

Keywords

  • Bayesian nonparametrics
  • Dirichlet process
  • Maximum a posteriori estimation
  • Quantum annealing
  • Stochastic optimization

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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