Estimating features with missing values and outliers: a bregman-proximal point algorithm for robust non-negative matrix factorization with application to gene expression analysis

HIGHLIGHTS

• who: Chrétien et_al BMC Bioinformatics et al. from the (UNIVERSITY) have published the Article: Estimating features with missing values and outliers: a Bregman-proximal point algorithm for robust Non-negative Matrix Factorization with application to gene expression analysis, in the Journal: (JOURNAL)
• what: The work described in is is a very interesting contribution to the study of potential convexifications for the NMF problem. In the present work, an efficient method is proposed that denoises the data, estimates the missing values, and identifies the outliers in M via nonnegative low-rank + sparse + noise matrix factorization . . .
  

   

  

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