Title: Learning with Correntropy-induced Losses for Regression with Mixture of Symmetric Stable Noise,
Abstract: In recent years, correntropy and its applications in machine learning have
been drawing continuous attention owing to its merits in dealing with
non-Gaussian noise and outliers. However, theoretical understanding of
correntropy, especially in the statistical learning context, is still limited.
In this study, within the statistical learning framework, we investigate
correntropy based regression in the presence of non-Gaussian noise or outliers.
Motivated by the practical way of generating non-Gaussian noise or outliers, we
introduce mixture of symmetric stable noise, which include Gaussian noise,
Cauchy noise, and their mixture as special cases, to model non-Gaussian noise
or outliers. We demonstrate that under the mixture of symmetric stable noise
assumption, correntropy based regression can learn the conditional mean
function or the conditional median function well without resorting to the
finite-variance or even the finite first-order moment condition on the noise.
In particular, for the above two cases, we establish asymptotic optimal
learning rates for correntropy based regression estimators that are
asymptotically of type $\mathcal{O}(n^{-1})$. These results justify the
effectiveness of the correntropy based regression estimators in dealing with
outliers as well as non-Gaussian noise. We believe that the present study
completes our understanding towards correntropy based regression from a
statistical learning viewpoint, and may also shed some light on robust
statistical learning for regression.