Quantum annealing for Dirichlet process mixture models with applications to network clustering
Quantum annealing for Dirichlet process mixture models with applications to network clustering
复制标题
狄利克雷过程混合模型的量子退火及其在网络聚类中的应用
DOI：
10.1016/j.neucom.2013.05.019
复制
发表时间：
2013
期刊：
影响因子：
6
通讯作者：
and Hiroshi Nakagawa
中科院分区：
文献类型：
--
作者：
Issei Sato;Shu Tanaka;Kenichi Kurihara;Seiji Miyashita;and Hiroshi Nakagawa
针对Dirichlet混合过程(DPM)模型，提出了一种基于中餐馆过程(CRP)的量子退火法(QA)。蚁群算法是模拟退火法的并行扩展，是一种并行随机优化技术。现有方法(栗原等人2009[12]和Sato et al.2009[20])不能适用于CRP，因为他们的QA框架是使用固定数量的混合物成分制定的。所提出的QA算法可以处理混合模型中不固定数量的类。我们将QA应用于DPM模型，用于聚集网络中的顶点，其中CRP座位安排指示网络分区。在多核处理器上进行了QA实验，实验结果表明，QA比SA、马尔可夫链蒙特卡罗推理法和波束搜索法能更好地对CRP中的座位安排进行最大后验估计。由于我们的QA算法和SA算法一样容易实现，因此它适用于广泛的应用。
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.