Conventional light microscopes have been used for centuries for the study of small length scales down to about 250 nanometers. At such a resolution level images are blurred and noisy and the data often can be well approximated by a Gaussian or Poisson model.
Learning from Highly Correlated Features using Graph Total Variation
Sparse models for machine learning have received substantial attention over the past two decades. Model selection, or determining which features are the best explanatory variables, is critical to the interpretability of a learned model.