IEEE Open Journal of Signal Processing (Jan 2020)

Robust Risk Minimization for Statistical Learning From Corrupted Data

  • Muhammad Osama,
  • Dave Zachariah,
  • Petre Stoica

DOI
https://doi.org/10.1109/OJSP.2020.3039632
Journal volume & issue
Vol. 1
pp. 287 – 294

Abstract

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We consider a general statistical learning problem where an unknown fraction of the training data is corrupted. We develop a robust learning method that only requires specifying an upper bound on the corrupted data fraction. The method minimizes a risk function defined by a non-parametric distribution with unknown probability weights. We derive and analyse the optimal weights and show how they provide robustness against corrupted data. Furthermore, we give a computationally efficient coordinate descent algorithm to solve the risk minimization problem. We demonstrate the wide range applicability of the method, including regression, classification, unsupervised learning and classic parameter estimation, with state-of-the-art performance.

Keywords