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New Insights Into Learning With Correntropy-Based Regression.

Yunlong Feng1

  • 1Department of Mathematics and Statistics, State University of New York at Albany, Albany, NY 12222, U.S.A. ylfeng@albany.edu.

Neural Computation
|October 20, 2020
PubMed
Summary
This summary is machine-generated.

Correntropy-based regression, derived from minimum distance estimation, robustly estimates conditional mean, mode, and median functions. New error bounds and convergence rates are established for learning the conditional mean.

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Area of Science:

  • Machine Learning
  • Information-Theoretic Learning
  • Statistical Learning

Background:

  • Correntropy criterion is a robust regression method.
  • It has successful real-world applications.
  • Recent studies explore its statistical learning properties.

Purpose of the Study:

  • Provide new insights into correntropy-based regression.
  • Unify regression approaches.
  • Establish error bounds and convergence rates for conditional mean learning.

Main Methods:

  • Deduce correntropy-based regression from minimum distance estimation.
  • Analyze its unified approach to conditional mean, mode, and median.
  • Develop error bounds and convergence rates under conditional moment assumptions.

Main Results:

  • Correntropy-based regression is a minimum distance estimator with robustness.
  • It unifies estimation of conditional mean, mode, and median.
  • New error bounds and exponential convergence rates are derived for conditional mean learning.
  • Saturation effect indicates inherent bias.

Conclusions:

  • Novel insights deepen understanding of correntropy-based regression.
  • Establishes a unified theoretical framework.
  • Enables investigation of learning schemes with nonconvex loss functions.