Computationally efficient learning of multivariate t mixture models with missing information

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题名:	Computationally efficient learning of multivariate t mixture models with missing information
作者:	Lin, T.-I.a , Ho, H.J.a, Shen, P.S.
贡献者:	Department of Statistics, Tunghai University Taichung
关键词:	Classifier;Learning with missing information;Multivariate t mixture models;Outlying observations;PX-EM algorithm
日期:	2009
上传时间:	2013-05-15T09:08:49Z (UTC)
摘要:	A finite mixture model using the multivariate t distribution has been well recognized as a robust extension of Gaussian mixtures. This paper presents an efficient PX-EM algorithm for supervised learning of multivariate t mixture models in the presence of missing values. To simplify the development of new theoretic results and facilitate the implementation of the PX-EM algorithm, two auxiliary indicator matrices are incorporated into the model and shown to be effective. The proposed methodology is a flexible mixture analyzer that allows practitioners to handle real-world multivariate data sets with complex missing patterns in a more efficient manner. The performance of computational aspects is investigated through a simulation study and the procedure is also applied to the analysis of real data with varying proportions of synthetic missing values. ? Springer-Verlag 2008.
關聯:	Computational Statistics Volume 24, Issue 3, 2009, Pages 375-392
显示于类别:	[統計學系所] 期刊論文

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