Sparse Linear Combination of SOMs for Data Imputation: Application to Financial Database
- Cite this paper as:
- Sorjamaa A. et al. (2009) Sparse Linear Combination of SOMs for Data Imputation: Application to Financial Database. In: Príncipe J.C., Miikkulainen R. (eds) Advances in Self-Organizing Maps. WSOM 2009. Lecture Notes in Computer Science, vol 5629. Springer, Berlin, Heidelberg
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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