Invention Grant
US08731881B2 Multivariate data mixture model estimation device, mixture model estimation method, and mixture model estimation program 有权
多变量数据混合模型估计装置,混合模型估计方法和混合模型估计程序

  • Patent Title: Multivariate data mixture model estimation device, mixture model estimation method, and mixture model estimation program
  • Patent Title (中): 多变量数据混合模型估计装置,混合模型估计方法和混合模型估计程序
  • Application No.: US13824857
    Application Date: 2012-03-16
  • Publication No.: US08731881B2
    Publication Date: 2014-05-20
  • Inventor: Ryohei FujimakiSatoshi Morinaga
  • Applicant: Ryohei FujimakiSatoshi Morinaga
  • Applicant Address: JP Tokyo
  • Assignee: NEC Corporation
  • Current Assignee: NEC Corporation
  • Current Assignee Address: JP Tokyo
  • Agency: Sughrue Mion, PLLC
  • Priority: JP2011-060732 20110318
  • International Application: PCT/JP2012/056862 WO 20120316
  • International Announcement: WO2012/128207 WO 20120927
  • Main IPC: G06K9/62
  • IPC: G06K9/62 G06F17/18
Multivariate data mixture model estimation device, mixture model estimation method, and mixture model estimation program
Abstract:
With respect to the model selection issue of a mixture model, the present invention performs high-speed model selection under an appropriate standard regarding the number of model candidates which exponentially increases as the number and the types to be mixed increase. A mixture model estimation device comprises: a data input unit to which data of a mixture model to be estimated, candidate values of the number of mixtures which are required for estimating the mixture model of the data, and types of components configuring the mixture model and parameters thereof, are input; a processing unit which sets the number of mixtures from the candidate values, calculates, with respect to the set number of mixtures, a variation probability of a hidden variable for a random variable which becomes a target for mixture model estimation of the data, and estimates the optimal mixture model by optimizing the types of the components and the parameters therefor using the calculated variation probability of the hidden variable so that the lower bound of the posterior probabilities of the model separated for each component of the mixture model can be maximized; and a model estimation result output unit which outputs the model estimation result obtained by the processing unit.
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