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Robust Singular Value Decomposition BaLsed on Weighted Least Absolute Deviation Regression
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 Title & Authors
Robust Singular Value Decomposition BaLsed on Weighted Least Absolute Deviation Regression
Jung, Kang-Mo;
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 Abstract
The singular value decomposition of a rectangular matrix is a basic tool to understand the structure of the data and particularly the relationship between row and column factors. However, conventional singular value decomposition used the least squares method and is not robust to outliers. We propose a simple robust singular value decomposition algorithm based on the weighted least absolute deviation which is not sensitive to leverage points. Its implementation is easy and the computation time is reasonably low. Numerical results give the data structure and the outlying information.
 Keywords
Outliers;robust statistics;singular value decomposition;weighted least absolute deviation;
 Language
English
 Cited by
1.
A robust AMMI model for the analysis of genotype-by-environment data, Bioinformatics, 2015, btv533  crossref(new windwow)
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