Optimized linear imputation

Yehezkel S. Resheff, Daphna Weinshall

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Often in real-world datasets, especially in high dimensional data, some feature values are missing. Since most data analysis and statistical methods do not handle gracefully missing values, the first step in the analysis requires the imputation of missing values. Indeed, there has been a long standing interest in methods for the imputation of missing values as a pre-processing step. One recent and effective approach, the IRMI stepwise regression imputation method, uses a linear regression model for each real-valued feature on the basis of all other features in the dataset. However, the proposed iterative formulation lacks convergence guarantee. Here we propose a closely related method, stated as a single optimization problem and a block coordinate-descent solution which is guaranteed to converge to a local minimum. Experiments show results on both synthetic and benchmark datasets, which are comparable to the results of the IRMI method whenever it converges. However, while in the set of experiments described here IRMI often diverges, the performance of our methods is shown to be markedly superior in comparison with other methods.

Original languageAmerican English
Title of host publicationICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
EditorsMaria De De Marsico, Gabriella Sanniti di Baja, Ana Fred
PublisherSciTePress
Pages17-25
Number of pages9
ISBN (Electronic)9789897582226
DOIs
StatePublished - 2017
Event6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017 - Porto, Portugal
Duration: 24 Feb 201726 Feb 2017

Publication series

NameICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
Volume2017-January

Conference

Conference6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
Country/TerritoryPortugal
CityPorto
Period24/02/1726/02/17

Bibliographical note

Publisher Copyright:
© 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

Keywords

  • Imputation

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