PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An improved methodology for filling missing values in spatiotemporal climate data set
Antti Sorjamaa, Amaury Lendasse, Yves Cornet and Eric Deleersnijder
Computational Geosciences 2009.


In this paper, an improved methodology for the determination of missing values in a spatiotemporal database is presented. This methodology performs denoising projection in order to accurately fill the missing values in the database. The improved methodology is called empirical orthogonal functions (EOF) pruning, and it is based on an original linear projection method called empirical orthogonal functions (EOF). The experiments demonstrate the performance of the improved methodology and present a comparison with the original EOF and with a widely used optimal interpolation method called objective analysis.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4917
Deposited By:Amaury Lendasse
Deposited On:24 March 2009