| Affiliation: | 1.National Physical Laboratory,Teddington, Middlesex,UK;2.FEMTO-ST Institute, UMR 6174 CNRS, DISC Computer Science Department,Université de Bourgogne Franche-Comté,Besan?on,France;3.ISIFC,Université de Bourgogne Franche-Comté,Besan?on,France;4.EA 3922/IFR133, UFR Sciences et Techniques,Université de Bourgogne Franche-Comté,Besan?on,France;5.ABC&T,Besan?on,France;6.Service de Biochimie et Biologie Moléculaire Sud, Pavillon 3D,Centre Hospitalier Lyon Sud,Lyon,France |
| Abstract: | BackgroundNon-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.ResultsAn 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 ? 1 penality.ConclusionsAn application to the analysis of gene expression data of patients with bladder cancer is finally proposed. |
