OUTLIER-ROBUST KERNEL HIERARCHICAL-OPTIMIZATION RLS ON A BUDGET WITH
AFFINE CONSTRAINTS
Abstract
This paper introduces a non-parametric learning framework to combat
outliers in online, multi-output, and nonlinear regression tasks. A
hierarchical-optimization problem underpins the learning task: Search in
a reproducing kernel Hilbert space (RKHS) for a function that minimizes
a sample average $\ell_p$-norm ($1
\leq p \leq 2$) error loss on data
contaminated by noise and outliers, subject to side information that
takes the form of affine constraints defined as the set of minimizers of
a quadratic loss on a finite number of faithful data devoid of noise and
outliers. To surmount the computational obstacles inflicted by the
choice of loss and the potentially infinite dimensional RKHS,
approximations of the $\ell_p$-norm loss, as well as a
novel twist of the criterion of approximate linear dependency are
devised to keep the computational-complexity footprint of the proposed
algorithm bounded over time. Numerical tests on datasets showcase the
robust behavior of the advocated framework against different types of
outliers, under a low computational load, while satisfying at the same
time the affine constraints, in contrast to the state-of-the-art methods
which are constraint agnostic.
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