空军工程大学学报（自然科学版）2017，Vol.18Issue(2)：89-94,6.DOI:10.3969/j.issn.1009-3516.2017.02.015
基于变分贝叶斯的数据分类算法
A Data Classification Algorithm Based on Variational Bayesian
摘要
Abstract
With the rapid development of Internet technology,the size and complexity of the database are continually growing,the traditional classification method can no longer meet the demand of the classification of complex data.For this reason,a data classification algorithm based on variational Bayesian is proposed.This paper introduces the variational approximation theory on the basis of traditional Bayesian inference,combines with the thought of maximum expected algorithm,utilizes the mean field theory in the statistical physics,and simulates taking Gaussian mixture model as an example.The experimental results show that the randomly generated data are composed of the three Gaussian models mixed after 382 iterations,the lower bound of likelihood function rises with the increase of iteration number,the curve becomes flat as expectation after 350 iterations,and the mean value and the inverse of covariance matrix close to the real data are obtained in the range of allowable error.Under the requirement of high precision,the calculation speed is faster,calculation efficiency is higher,and all of these accord with the demands of actual engineering application background.关键词
变分贝叶斯/分类算法/最大期望算法Key words
variational Bayesian/classification algorithm/EM algorithm分类
信息技术与安全科学引用本文复制引用
张文倩,王瑛,张红梅,宋增杰..基于变分贝叶斯的数据分类算法[J].空军工程大学学报（自然科学版）,2017,18(2):89-94,6.基金项目
国家自然科学基金(71171199) （71171199）
