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Activity Number:
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251
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #308193 |
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Title:
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On Efficient Supervised Learning of Multivariate t Mixture Models with Missing Information
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Author(s):
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Tsung-I Lin*+ and Hsiu-J Ho and Pao-S Shen
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Companies:
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National Chung Hsing University and National Chung Hsing University and Tunghai University
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Address:
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Department of Applied Mathematics, Taichung, 402, Taiwan
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Keywords:
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Classifier ; Learning with missing information ; Missing values ; Multivariate t mixture model ; PX-EM algorithm ; Outlying observations
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Abstract:
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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 more efficient manners. The performance of computational aspects is investigated through a simulation study and the procedure is also applied to two real data sets with varying proportions of synthetic missing values.
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