自动化学报2011，Vol.37Issue(4)：389-407,19.DOI:10.3724/SP.J.1004.2011.00389
分层Dirichlet过程及其应用综述
Hierarchical Dirichlet Processes and Their Applications: A Survey
摘要
Abstract
Dirichlet processes are a type of stochastic processes widely used in nonparametric Bayesian models, especially in research that involves probabilistic graphical models. Over the past few years, significant effort has been made in the study of such processes, mainly due to their modeling flexibility and wide applicability. For instance, Dirichlet processes are capable of learning the number of clusters as well as the corresponding parameters of each cluster whereas other clustering or classification models usually are not able to. In this survey, we first introduce the definitions of Dirichlet processes. We then present Dirichlet process mixture models and their applications, and discuss in detail hierarchical Dirichlet processes (HDP), their roles in constructing other models, and examples of related applications in many important fields. Finally,we summarize recent developments in the study and applications of hierarchical Dirichlet processes and offer our remarks on future research.关键词
Dirichlet过程/概率图模型/聚类/分层Dirichlet过程Key words
Dirichlet processes/ probabilistic graphical models/ clustering/ hierarchical Dirichlet processes (HDP)引用本文复制引用
周建英,王飞跃,曾大军..分层Dirichlet过程及其应用综述[J].自动化学报,2011,37(4):389-407,19.基金项目
国家自然科学基金(70890084,60921061,71025001)资助 （70890084,60921061,71025001）
