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.