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

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.

• Type: Conference
• Year: 2009
• Where: WSOM
• Authors: Antti Sorjamaa, Francesco Corona, Yoan Miche, Paul Merlin, Bertrand Maillet, Eric Séverin, Amaury Lendasse
• Keywords: Artificial Intelligence, Lengthy Cross-validation Procedure, Methodology, Missing Values, WSOM 2009