Logo image
Back
Robust kernels for robust location estimation
Journal article   Open access   Peer reviewed

Robust kernels for robust location estimation

Joseph A. Gallego, Fabio A. Gonzalez and Olfa Nasraoui
Neurocomputing (Amsterdam), v 429, pp 174-186
14 Mar 2021
DOI: https://doi.org/10.1016/j.neucom.2020.10.090
url
https://zenodo.org/record/7135709View
Open

Abstract

Computer Science Computer Science, Artificial Intelligence Science & Technology Technology
This paper shows that least-square estimation (mean calculation) in a reproducing kernel Hilbert space (RKHS) F corresponds to different M-estimators in the original space depending on the kernel function associated with F. In particular, we present a proof of the correspondence of mean estimation in an RKHS for the Gaussian kernel with robust estimation in the original space performed with the Welsch Mestimator. This result is generalized to other types of M-estimators. This generalization facilitates the definition of new robust kernels associated to Huber, Tukey, Cauchy and Andrews M-estimators. The new kernels are empirically evaluated in different clustering tasks where state-of-the-art robust clustering methods are compared to kernel-based clustering using robust kernels. The results show that some robust kernels perform on a par with the best state-of-the-art robust clustering methods. (C) 2020 Elsevier B.V. All rights reserved.

Metrics

4 Record Views
3 citations in Web of Science

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Logo image