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Gibbs parameter sampling method applied to a random point mode finite hybrid model

A hybrid model, random point technology, applied in the field of pattern recognition, can solve the problem that the EM algorithm is easily affected by the initial value

Inactive Publication Date: 2020-07-03
HANGZHOU DIANZI UNIV
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Problems solved by technology

In previous related studies, it was proposed to use EM algorithm and Gibbs sampling algorithm to solve related problems, and EM algorithm is easily affected by the initial value

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  • Gibbs parameter sampling method applied to a random point mode finite hybrid model
  • Gibbs parameter sampling method applied to a random point mode finite hybrid model
  • Gibbs parameter sampling method applied to a random point mode finite hybrid model

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Embodiment Construction

[0046] A Gibbs parameter sampling method applied to a random point mode finite mixture model, specifically as follows:

[0047] Step (1). According to the characteristics of the random point pattern, construct the random point pattern finite mixture model:

[0048] A point-mode mixture model with K sources of randomness is expressed as:

[0049] f(X n |Θ)=π 1 f(X n |θ 1 )+π 2 f(X n |θ 2 )+…+π K f(X n |θ K );

[0050] x n Indicates the nth random point mode observation data, n=1,2,...,N, N is the number of random point mode observation data, Represents the finite set space of R, and R is the space of real numbers;

[0051] Parameter set Θ={π for point-mode mixed model 1 , π 2 ,…,π K ,θ 1 ,θ 2 ,…,θ K}∈(R + ×Θ) K , R + Represents positive real number space; {θ 1 ,θ 2 ,…,θ K} is the parameter variable in the random point pattern distribution function, {π 1 , π 2 ,…,π K} is the mixing weight, π k is the mixing weight of the kth distribution element, ...

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Abstract

The invention relates to a Gibbs parameter sampling method applied to a random point mode finite hybrid model. The method comprises the steps that firstly, a random point mode finite hybrid model anda random point mode likelihood function are constructed, then random point mode finite hybrid model parameter prior distribution is constructed, and posterior distribution of model parameters is obtained according to the model parameter prior distribution; and finally, estimating the number of distribution elements in mixed distribution and model parameter values by adopting a sampling algorithm combining a Gibbs sampling algorithm and a Bayesian information criterion. Compared with the traditional FMM which only describes the characteristic randomness of the data, the random point mode distribution function also describes the cardinal number randomness of the data; on the basis of RPP-FMM, a Gibbs sampling algorithm is adopted to sample sample data to obtain model parameters, and the situation that parameter estimation may fall into a local extreme point all the time, and a global extreme point cannot be obtained is avoided. According to the method, the modeling precision and the parameter estimation precision are effectively improved.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a Gibbs (Gibbs) parameter sampling method applied to a random point pattern finite mixture model. Background technique [0002] The finite mixture model (FMM) is a statistical modeling tool that provides an efficient mathematical method of simulating complex densities with simple densities. There are two core issues in finite mixture models: the selection of mixture component densities and the parameter estimation of mixture models. Due to its simple form and convenient calculation, the Gaussian mixture model has become a finite mixture model commonly used at present. However, most of the actual data we get has nonlinear and non-Gaussian characteristics, and is limited to the fitting ability of Gaussian distribution, which makes the Gaussian mixture model unable to completely, accurately and effectively describe these complex data. According to the numbe...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N7/00G06F17/18
CPCG06F17/18G06N7/01
Inventor 刘伟峰王志黄梓龙丁禹心
Owner HANGZHOU DIANZI UNIV
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