PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Sparse Linear Combination of SOMs for Data Imputation: Application to Financial Database
Antti Sorjamaa, Francesco Corona, Yoan Miche, Paul Merlin, Bertrand Maillet, Eric Séverin and Amaury Lendasse
In: Advances in Self-Organizing Maps Lecture Notes in Computer Science . (2009) Springer Berlin / Heidelberg , pp. 290-297. ISBN 978-3-642-02396-5


This paper presents a new methodology for missing value imputation in a database. The methodology combines the outputs of several Self-Organizing Maps in order to obtain an accurate filling for the missing values. The maps are combined using MultiResponse Sparse Regression and the Hannan-Quinn Information Criterion. The new combination methodology removes the need for any lengthy cross-validation procedure, thus speeding up the computation significantly. Furthermore, the accuracy of the filling is improved, as demonstrated in the experiments.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6664
Deposited By:Amaury Lendasse
Deposited On:08 March 2010