DeepAI AI Chat
Log In Sign Up

Robustness of Maximum Correntropy Estimation Against Large Outliers

by   Badong Chen, et al.
Xi'an Jiaotong University
University of Florida
Southwest Jiaotong University

The maximum correntropy criterion (MCC) has recently been successfully applied in robust regression, classification and adaptive filtering, where the correntropy is maximized instead of minimizing the well-known mean square error (MSE) to improve the robustness with respect to outliers (or impulsive noises). Considerable efforts have been devoted to develop various robust adaptive algorithms under MCC, but so far little insight has been gained as to how the optimal solution will be affected by outliers. In this work, we study this problem in the context of parameter estimation for a simple linear errors-in-variables (EIV) model where all variables are scalar. Under certain conditions, we derive an upper bound on the absolute value of the estimation error and show that the optimal solution under MCC can be very close to the true value of the unknown parameter even with outliers (whose values can be arbitrarily large) in both input and output variables. Illustrative examples are presented to verify and clarify the theory.


Constrained Maximum Correntropy Adaptive Filtering

Constrained adaptive filtering algorithms inculding constrained least me...

Diffusion Maximum Correntropy Criterion Algorithms for Robust Distributed Estimation

Robust diffusion adaptive estimation algorithms based on the maximum cor...

Broad Learning System Based on Maximum Correntropy Criterion

As an effective and efficient discriminative learning method, Broad Lear...

Supplementary Material for CDC Submission No. 1461

In this paper, we focus on the influences of the condition number of the...

RCR: Robust Compound Regression for Robust Estimation of Errors-in-Variables Model

The errors-in-variables (EIV) regression model, being more realistic by ...

Tightly Robust Optimization via Empirical Domain Reduction

Data-driven decision-making is performed by solving a parameterized opti...

ROBIN: a Graph-Theoretic Approach to Reject Outliers in Robust Estimation using Invariants

Many estimation problems in robotics, computer vision, and learning requ...