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Lookup NU author(s): Dr Tao Chen, Professor Elaine Martin, Professor Gary Montague
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Principal component analysis (PCA) is a widely adopted multivariate data analysis technique, with interpretation being established on the basis of both classical linear projection and a probability model (i.e. probabilistic PCA (PPCA)). Recently robust PPCA models, by using the multivariate t-distribution, have been proposed to consider the situation where there may be outliers within the data set. This paper presents an overview of the robust PPCA technique, and further discusses the issue of missing data. An expectation-maximization (EM) algorithm is presented for the maximum likelihood estimation of the model parameters in the presence of missing data. When applying robust PPCA for outlier detection, a contribution analysis method is proposed to identify which variables contribute the most to the occurrence of outliers, providing valuable information regarding the source of outlying data. The proposed technique is demonstrated on numerical examples, and the application to outlier detection and diagnosis in an industrial fermentation process. (C) 2009 Elsevier B.V. All rights reserved.
Author(s): Chen T, Martin E, Montague G
Publication type: Article
Publication status: Published
Journal: Computational Statistics & Data Analysis
Year: 2009
Volume: 53
Issue: 10
Pages: 3706-3716
ISSN (print): 0167-9473
ISSN (electronic):
Publisher: Elsevier
URL: http://dx.doi.org/10.1016/j.csda.2009.03.014
DOI: 10.1016/j.csda.2009.03.014
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