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Related Experiment Video

Updated: Sep 25, 2025

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
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Missing value imputation using least squares techniques in contaminated matrices.

Marisol Garcia-Peña1, Sergio Arciniegas-Alarcón2, Wojtek J Krzanowski3

  • 1Pontificia Universidad Javeriana, Departamento de Matemáticas, Bogotá, Colombia.

Methodsx
|April 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces robust methods to improve data imputation quality by handling outliers. Pre-processing techniques like robust singular value decomposition prevent data errors from affecting imputation results.

Keywords:
Cross-validationEigenvaluesEigenvectorsGenotype-by-environment interactionIterative computational schemeMissing valuesRobust singular value decomposition

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Area of Science:

  • Data Science
  • Statistical Modeling
  • Matrix Approximation

Background:

  • Matrix imputation methods using least squares can be sensitive to outliers.
  • Outliers can degrade imputation quality and cause convergence issues.

Purpose of the Study:

  • To develop and evaluate pre-processing strategies for robust data imputation.
  • To mitigate the impact of outliers on matrix imputation algorithms.

Main Methods:

  • Explored pre-processing options before applying a mixture of regression and lower rank approximation.
  • Investigated robust singular value decomposition (SVD) and outlier detection followed by treating outliers as missing data.
  • Evaluated methods using cross-validation on real-world multi-environment trial data.

Main Results:

  • Proposed pre-processing methods significantly improve imputation quality compared to the original algorithm.
  • Robust SVD effectively handles outliers and enhances the imputation procedure.
  • Outlier detection and treatment as missing data also yielded robust imputation results.

Conclusions:

  • The original imputation method is susceptible to outliers and should be replaced by robust alternatives.
  • Pre-processing steps are crucial for reliable matrix imputation, especially with suspected data contamination.
  • The proposed methods ensure algorithm performance on datasets with potential outlier contamination.