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

  • Antti Sorjamaa
  • Francesco Corona
  • Yoan Miche
  • Paul Merlin
  • Bertrand Maillet
  • Eric Séverin
  • Amaury Lendasse
Conference paper

DOI: 10.1007/978-3-642-02397-2_33

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5629)
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

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Antti Sorjamaa
    • 1
  • Francesco Corona
    • 1
  • Yoan Miche
    • 1
  • Paul Merlin
    • 2
  • Bertrand Maillet
    • 2
  • Eric Séverin
    • 3
  • Amaury Lendasse
    • 2
  1. 1.Department of Information and Computer ScienceHelsinki University of TechnologyFinland
  2. 2.A.A. Advisors-QCG (ABN AMRO) – Variances, CES/CNRS and EIFUniversity of Paris-1
  3. 3.Department GEAUniversity of Lille 1France

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