Cheap robust learning of data anomalies with analytically solvable entropic outlier sparsification [Applied Mathematics]

Entropic outlier sparsification (EOS) is proposed as a cheap and robust computational strategy for learning in the presence of data anomalies and outliers. EOS dwells on the derived analytic solution of the (weighted) expected loss minimization problem subject to Shannon entropy regularization. An identified closed-form solution is proven to impose...

Source: Proceedings of the National Academy of Sciences - February 23, 2022 Category: Science Authors: Illia Horenko Tags: Applied Mathematics, Brief Reports Physical Sciences Source Type: research