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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

Overview of attention for article published in BMC Bioinformatics, August 2016
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Mentioned by

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2 tweeters

Citations

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4 Dimensions

Readers on

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6 Mendeley
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Title
A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1120-8
Pubmed ID
Authors

Stéphane Chrétien, Christophe Guyeux, Bastien Conesa, Régis Delage-Mouroux, Michèle Jouvenot, Philippe Huetz, Françoise Descôtes

Abstract

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. 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. An application to the analysis of gene expression data of patients with bladder cancer is finally proposed.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 17%
Student > Bachelor 1 17%
Researcher 1 17%
Student > Postgraduate 1 17%
Unknown 2 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 33%
Mathematics 1 17%
Medicine and Dentistry 1 17%
Unknown 2 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 August 2016.
All research outputs
#4,181,684
of 8,307,635 outputs
Outputs from BMC Bioinformatics
#2,360
of 3,627 outputs
Outputs of similar age
#127,780
of 251,640 outputs
Outputs of similar age from BMC Bioinformatics
#81
of 138 outputs
Altmetric has tracked 8,307,635 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,627 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.