Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting

@article{Tepper2016NonnegativeMU,
  title={Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting},
  author={Mariano Tepper and Guillermo Sapiro},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={655-663}
}
  • Mariano TepperG. Sapiro
  • Published 4 November 2016
  • Computer Science
  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
In this work, we introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i.e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint. NMU results are interesting as, compared to traditional NMF, they present additional sparsity and part-based behavior, explaining unique data features. To show these features in practice, we first present an application to the analysis of climate data. We then present an NMU… 
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