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有限贝塔刘维尔混合模型的变分学习及其应用
  • ISSN号：0372-2112
  • 期刊名称：《电子学报》
  • 时间：0
  • 分类：TP391.4[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]北京邮电大学计算机学院,北京100876, [2]云南大学信息学院,云南昆明650091, [3]北京邮电大学理学院,北京100876
  • 相关基金：国家自然科学基金（No.61121061,No.60972077,No.61072079,No.61363085）; 教育部博士点基金（No.20120005110017）; 国家863高技术研究发展计划（No.2009AA01Z430）
作者： 赖裕平[1], 丁洪伟[2], 周亚建[1], 郭玉翠[3], 杨义先[1]
关键词： 有限混合模型, 贝塔刘维尔分布, 分解近似, 模型选择, 变分推断, finite mixture models, Beta-LiouviUe distribution, factorized approximation, model selection, variational inference
中文摘要：

由于贝塔刘维尔分布的共轭先验分布中存在积分表达式,贝叶斯估计有限贝塔刘维尔混合模型参数异常困难.本文提出利用变分贝叶斯学习模型参数,采用gamma分布作为近似的先验分布并使用合理的非线性近似技术,得到了后验分布的近似解.与常用的EM算法相比,该方法能够同时估计模型参数和确定分量数,且避免了过拟合的问题.在合成数据集及场景分类问题上进行了大量的实验,实验结果验证了本文所提方法的有效性.

英文摘要：

Since the integration expression is present in the conjugate prior distribution,Bayesian estimation of the parameters in finite Beta-Liouville mixture models（BLM）is analytically inlractable. In this paper, an approach based on the variational inference framework is proposed. Adopting gamma distributions to approximate the prior distributions of the parameter in BI.aM and using some reasonable non-linear approximations; the closed form solution for the posterior distribution of the parameters is obtained. Compared to the conventional expectation maximization （EM）algorithm, the proposed algorithm is able to simultaneously estimate the model parameters and determine the number of components; our method also avoids the problem of overfitting. Extensive experi- mental results based on the synthetic data sets and scenes classification show that the proposed method is efficient and feasible in terms of parameter estimation and model selection.

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期刊信息
  • 《电子学报》
  • 中国科技核心期刊
  • 主管单位:中国科学技术协会
  • 主办单位:中国电子学会
  • 主编：郝跃
  • 地址：北京165信箱
  • 邮编：100036
  • 邮箱：new@ejournal.org.cn
  • 电话：010-68279116 68285082
  • 国际标准刊号：ISSN：0372-2112
  • 国内统一刊号：ISSN：11-2087/TN
  • 邮发代号:2-891
  • 获奖情况:
  • 2000年获国家期刊奖,2000年获国家自然科学基金志项基金支持,中国期刊方阵“双高”期刊
  • 国内外数据库收录:
  • 美国化学文摘（网络版）,荷兰文摘与引文数据库,美国工程索引,美国剑桥科学文摘,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,英国英国皇家化学学会文摘,中国北大核心期刊（2000版）
  • 被引量:57611