Computationally efficient learning of multivariate t mixture models with missing information

Authors:
Tsung-I Lin;Hsiu J. Ho;Pao S. Shen
Affiliations:
National Chung Hsing University, Department of Applied Mathematics, 402, Taichung, Taiwan;National Chung Hsing University, Department of Applied Mathematics, 402, Taichung, Taiwan;Tunghai University, Department of Statistics, PO Box 823, 407, Taichung, Taiwan
Venue:
Computational Statistics
Year:
2009

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Abstract

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.