Predicting missing values with biclustering: A coherence-based approach

Authors:
F. O. De FrançA;G. P. Coelho;F. J. Von Zuben
Affiliations:
Center of Mathematics, Computing and Cognition (CMCC), Federal University of ABC (UFABC), Santo André, SP, Brazil and Laboratory of Bioinformatics and Bioinspired Computing (LBiC), Department ...;Laboratory of Natural Computing (LCoN-FT), School of Technology (FT), University of Campinas (Unicamp), Limeira, SP, Brazil and Laboratory of Bioinformatics and Bioinspired Computing (LBiC), Depar ...;Laboratory of Bioinformatics and Bioinspired Computing (LBiC), Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering (FEEC), University ...
Venue:
Pattern Recognition
Year:
2013

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Abstract

In this work, a novel biclustering-based approach to data imputation is proposed. This approach is based on the Mean Squared Residue metric, used to evaluate the degree of coherence among objects of a dataset, and presents an algebraic development that allows the modeling of the predictor as a quadratic programming problem. The proposed methodology is positioned in the field of missing data, its theoretical aspects are discussed and artificial and real-case scenarios are simulated to evaluate the performance of the technique. Additionally, relevant properties introduced by the biclustering process are also explored in post-imputation analysis, to highlight other advantages of the proposed methodology, more specifically confidence estimation and interpretability of the imputation process.