Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/37052
標題: Computationally efficient learning of multivariate t mixture models with missing information
作者: Lin, T.I.
林宗儀
Ho, H.J.
Shen, P.S.
關鍵字: Classifier
Learning with missing information
Multivariate t mixture
models
PX-EM algorithm
Outlying observations
em algorithm
maximum-likelihood
bayesian-analysis
cluster-analysis
unknown number
distributions
robust
inference
ecm
components
期刊/報告no:: Computational Statistics, Volume 24, Issue 3, Page(s) 375-392.
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.
URI: http://hdl.handle.net/11455/37052
ISSN: 0943-4062
文章連結: http://dx.doi.org/10.1007/s00180-008-0129-5
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