Mixtures of spatial spline regressions for clustering and classification

Nguyen, Hien D., McLachlan, Geoffrey J. and Wood, Ian A. (2016) Mixtures of spatial spline regressions for clustering and classification. Computational Statistics and Data Analysis, 93 76-85. doi:10.1016/j.csda.2014.01.011

Author Nguyen, Hien D.
McLachlan, Geoffrey J.
Wood, Ian A.
Title Mixtures of spatial spline regressions for clustering and classification
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
Publication date 2016-01
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.csda.2014.01.011
Open Access Status Not Open Access
Volume 93
Start page 76
End page 85
Total pages 10
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2015
Language eng
Abstract Classification and clustering of functional data arise in many areas of modern research. Currently, techniques for performing such tasks have concentrated on applications to univariate functions. Such techniques can be extended to the domain of classifying and clustering bivariate functions (i.e. surfaces) over rectangular domains. This is achieved by combining the current techniques in spatial spline regression (SSR) with finite mixture models and mixed-effects models. As a result, three novel techniques have been developed: spatial spline mixed models (SSMM) for fitting populations of surfaces, mixtures of SSR (MSSR) for clustering surfaces, and MSSR discriminant analysis (MSSRDA) for classification of surfaces. Through simulations and applications to problems in handwritten character recognition, it is shown that SSMM, MSSR, and MSSRDA are effective in performing their desired tasks. It is also shown that in the context of handwritten character recognition, MSSR and MSSRDA are comparable to established methods, and are able to outperform competing approaches in missing-data situations.
Keyword Functional data
Mixture model
Spatial spline
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2015 Collection
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Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 3 times in Scopus Article | Citations
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