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We consider the task of robust nonlinear regression in the presence of both inlier noise and outliers. Assuming that the unknown nonlinear function belongs to a reproducing kernel Hilbert space, our goal is to estimate the set of the associated unknown parameters. Due to the presence of outliers, common techniques such as the kernel ridge regression (KRR) or the support vector regression turn out to be inadequate. Instead, we employ sparse modeling arguments to explicitly model and estimate the outliers, adopting a greedy approach. The proposed robust scheme, i.e., kernel greedy algorithm for robust denoising (KGARD), is inspired by the classical Orthogonal matching pursuit (OMP) algorithm. Specifically, the proposed method alternates between a KRR task and an OMP-like selection step. Theoretical results concerning the identification of the outliers are provided. Moreover, KGARD is compared against other cutting edge methods, where its performance is evaluated via a set of experiments with various types of noise. Finally, the proposed robust estimation framework is applied to the task of image denoising, and its enhanced performance in the presence of outliers is demonstrated.

Robust Nonlinear Regression: A Greedy Approach Employing Kernels With Application to Image Denoising