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混合因子分析的重新抽样方法 被引量：2

The Resampling Method for Mixtures of Factor Analyzers
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摘要 混合因子分析是一种对具有复杂结构的多维数据建立模型的方法.本文提出了一种进行混合因子分析的重新抽样方法.当给定一组数据样本时,我们首先建立样本概率分布的混合高斯模型,然后为每一个高斯混合项重新抽取新的数据样本,在新的样本上再对每一个高斯混合项进行因子分析.与已有的算法相比较,避免了计算各个高斯混合项在每个样本值之下的后验概率,又减少了进行因子分析时参与计算的数据样本的数量. The mixtures of factor analyzers are able to model complex data structures through a combination of the factor analysis model and the Gaussian mixture model. In this paper, a resampling method for the mixtures of factor analyzers is proposed. After approximating the probability distribution density of the data by the Gaussian mixture model, we draw new samples for each of the component Gaussians with its own parameters separately, then on the new samples the factor analysis is performed for each component Gaussians. We also implement this method with the EM algorithm and the good performance of the method is illustrated by an example.
作者 岳博 焦李成
机构地区 西安电子科技大学雷达信号处理重点实验室
出处 《电子学报》 EI CAS CSCD 北大核心 2002年第12期1873-1875,共3页 Acta Electronica Sinica
基金 国家自然科学基金(No.60073053)
关键词 混合因子 重新抽样方法 因子分析 混合高斯模型 EM算法 学习模型 factor analysis Gaussian mixture model EM algorithm sampling
分类号 O212.4 [理学—概率论与数理统计]
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参考文献6

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同被引文献25

  • 1钟伟才,刘静,刘芳,焦李成.二阶卡尔曼滤波分布估计算法[J].计算机学报,2004,27(9):1272-1277. 被引量：6
  • 2成新民,沈律,赵力,邹采荣.基于修正EM算法的说话人识别的研究[J].电声技术,2004,28(12):51-53. 被引量：3
  • 3Rosti A-V I, Gales M J F. Factor analyzed hidden Markov models[A]. Proc. of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP)[C]. 2002, 1: 949-952.
  • 4Yao K, Paliwal K K, Lee T W. Generative factor analyzed HMM for automatic speech recognition[J]. Speech Communication, 2005, 45: 435-454.
  • 5LEI Xiongguo, LI Ling, ZENG Yicheng. Text-independent speaker identification using factor analyzed hidden Markov model[A]. NCMMSC'05[C]. 2005, 24: 222-225.
  • 6Reynolds D A, Quatieri T, Dunn R. Speaker verification using adapted Gaussian mixture models[J]. Digital Signal Processing, 2000, (10): 19-41.
  • 7Lawrence Saul, Mazin Rahim. Maximum likelihood and minimum classification error factor analysis for automatic speech recognition[J]. IEEE Transactions on Speech and Audio Processing, 2000, 8(2): 115-125.
  • 8Saul L, Rahim M. Maximum likelihood and minimum classification error factor analysis for automatic speech recognition[J]. IEEE Transactions on Speech and Audio Processing, 1999, 8(2): 115-125.
  • 9Chickering D M. Learning Bayesian networks is NP-complete[ A]. Learning from Data: Artificial Intelligence and Statistics V[M]. New York, USA: Springer, 1996. 121-130.
  • 10Muhlenbein H, Mahnig T. FDA - a scalable evolutionary algorithm for the optimization of additively decomposed functions [ J].Evolutionary Computation, 1999, 7(4) : 353 - 376.

引证文献2

电子学报
2002年 第12期

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