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A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis
BACKGROUND: Non-Negative Matrix factorization has become an essential tool for feature extraction in a wide spectrum of applications. In the present work, our objective is to extend the applicability of the method to the case of missing and/or corrupted data due to outliers. RESULTS: An essential pr...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009666/ https://www.ncbi.nlm.nih.gov/pubmed/27585655 http://dx.doi.org/10.1186/s12859-016-1120-8 |
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author | Chrétien, Stéphane Guyeux, Christophe Conesa, Bastien Delage-Mouroux, Régis Jouvenot, Michèle Huetz, Philippe Descôtes, Françoise |
author_facet | Chrétien, Stéphane Guyeux, Christophe Conesa, Bastien Delage-Mouroux, Régis Jouvenot, Michèle Huetz, Philippe Descôtes, Françoise |
author_sort | Chrétien, Stéphane |
collection | PubMed |
description | BACKGROUND: Non-Negative Matrix factorization has become an essential tool for feature extraction in a wide spectrum of applications. In the present work, our objective is to extend the applicability of the method to the case of missing and/or corrupted data due to outliers. RESULTS: An essential property for missing data imputation and detection of outliers is that the uncorrupted data matrix is low rank, i.e. has only a small number of degrees of freedom. We devise a new version of the Bregman proximal idea which preserves nonnegativity and mix it with the Augmented Lagrangian approach for simultaneous reconstruction of the features of interest and detection of the outliers using a sparsity promoting ℓ(1) penality. CONCLUSIONS: An application to the analysis of gene expression data of patients with bladder cancer is finally proposed. |
format | Online Article Text |
id | pubmed-5009666 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50096662016-09-09 A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis Chrétien, Stéphane Guyeux, Christophe Conesa, Bastien Delage-Mouroux, Régis Jouvenot, Michèle Huetz, Philippe Descôtes, Françoise BMC Bioinformatics Research BACKGROUND: Non-Negative Matrix factorization has become an essential tool for feature extraction in a wide spectrum of applications. In the present work, our objective is to extend the applicability of the method to the case of missing and/or corrupted data due to outliers. RESULTS: An essential property for missing data imputation and detection of outliers is that the uncorrupted data matrix is low rank, i.e. has only a small number of degrees of freedom. We devise a new version of the Bregman proximal idea which preserves nonnegativity and mix it with the Augmented Lagrangian approach for simultaneous reconstruction of the features of interest and detection of the outliers using a sparsity promoting ℓ(1) penality. CONCLUSIONS: An application to the analysis of gene expression data of patients with bladder cancer is finally proposed. BioMed Central 2016-08-31 /pmc/articles/PMC5009666/ /pubmed/27585655 http://dx.doi.org/10.1186/s12859-016-1120-8 Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Chrétien, Stéphane Guyeux, Christophe Conesa, Bastien Delage-Mouroux, Régis Jouvenot, Michèle Huetz, Philippe Descôtes, Françoise A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis |
title | A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis |
title_full | A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis |
title_fullStr | A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis |
title_full_unstemmed | A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis |
title_short | A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis |
title_sort | bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009666/ https://www.ncbi.nlm.nih.gov/pubmed/27585655 http://dx.doi.org/10.1186/s12859-016-1120-8 |
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