PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Linear Combination of SOMs for Data Imputation: Application to Financial Problems
Antti Sorjamaa, Francesco Corona, Amaury Lendasse, Yoan Miche and Eric Severin
In: 7th International Workshop on Self-Organizing Maps (WSOM 2009), June 2009, Saint Augustine.


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:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4918
Deposited By:Amaury Lendasse
Deposited On:24 March 2009