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. © 2009 Springer-Verlag Berlin Heidelberg.
Recommended Citation
A. Sorjamaa et al., "Sparse Linear Combination of Soms for Data Imputation: Application to Financial Database," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5629 LNCS, pp. 290 - 297, Springer, Aug 2009.
The definitive version is available at https://doi.org/10.1007/978-3-642-02397-2_33
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-364202396-5
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
27 Aug 2009
