Sparse Linear Combination of SOMs for Data Imputation: Application to Financial Database

Authors:
Antti Sorjamaa;Francesco Corona;Yoan Miche;Paul Merlin;Bertrand Maillet;Eric Séverin;Amaury Lendasse
Affiliations:
Department of Information and Computer Science, Helsinki University of Technology, Finland;Department of Information and Computer Science, Helsinki University of Technology, Finland;Department of Information and Computer Science, Helsinki University of Technology, Finland;A.A. Advisors-QCG (ABN AMRO) --- Variances, CES/CNRS and EIF, University of Paris-1;A.A. Advisors-QCG (ABN AMRO) --- Variances, CES/CNRS and EIF, University of Paris-1;Department GEA, University of Lille 1, France;A.A. Advisors-QCG (ABN AMRO) --- Variances, CES/CNRS and EIF, University of Paris-1
Venue:
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Year:
2009

Citing 2
Cited 1

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Abstract

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.