58 research outputs found
Robustness analysis of a Maximum Correntropy framework for linear regression
In this paper we formulate a solution of the robust linear regression problem
in a general framework of correntropy maximization. Our formulation yields a
unified class of estimators which includes the Gaussian and Laplacian
kernel-based correntropy estimators as special cases. An analysis of the
robustness properties is then provided. The analysis includes a quantitative
characterization of the informativity degree of the regression which is
appropriate for studying the stability of the estimator. Using this tool, a
sufficient condition is expressed under which the parametric estimation error
is shown to be bounded. Explicit expression of the bound is given and
discussion on its numerical computation is supplied. For illustration purpose,
two special cases are numerically studied.Comment: 10 pages, 5 figures, To appear in Automatic
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals
The Partial Least Square Regression (PLSR) exhibits admirable competence for
predicting continuous variables from inter-correlated brain recordings in the
brain-computer interface. However, PLSR is in essence formulated based on the
least square criterion, thus, being non-robust with respect to noises. The aim
of this study is to propose a new robust implementation for PLSR. To this end,
the maximum correntropy criterion (MCC) is used to propose a new robust variant
of PLSR, called as Partial Maximum Correntropy Regression (PMCR). The
half-quadratic optimization is utilized to calculate the robust projectors for
the dimensionality reduction, and the regression coefficients are optimized by
a fixed-point approach. We evaluate the proposed PMCR with a synthetic example
and the public Neurotycho electrocorticography (ECoG) datasets. The extensive
experimental results demonstrate that, the proposed PMCR can achieve better
prediction performance than the conventional PLSR and existing variants with
three different performance indicators in high-dimensional and noisy regression
tasks. PMCR can suppress the performance degradation caused by the adverse
noise, ameliorating the decoding robustness of the brain-computer interface
Investigation of the performance of multi-input multi-output detectors based on deep learning in non-Gaussian environments
The next generation of wireless cellular communication networks must be energy efficient, extremely reliable, and have low latency, leading to the necessity of using algorithms based on deep neural networks (DNN) which have better bit error rate (BER) or symbol error rate (SER) performance than traditional complex multi-antenna or multi-input multi-output (MIMO) detectors. This paper examines deep neural networks and deep iterative detectors such as OAMP-Net based on information theory criteria such as maximum correntropy criterion (MCC) for the implementation of MIMO detectors in non-Gaussian environments, and the results illustrate that the proposed method has better BER or SER performance
Maximum Correntropy Ensemble Kalman Filter
In this article, a robust ensemble Kalman filter (EnKF) called MC-EnKF is
proposed for nonlinear state-space model to deal with filtering problems with
non-Gaussian observation noises. Our MC-EnKF is derived based on maximum
correntropy criterion (MCC) with some technical approximations. Moreover, we
propose an effective adaptive strategy for kernel bandwidth selection.Besides,
the relations between the common EnKF and MC-EnKF are given, i.e., MC-EnKF will
converge to the common EnKF when the kernel bandwidth tends to infinity. This
justification provides a complementary understanding of the kernel bandwidth
selection for MC-EnKF. In experiments, non-Gaussian observation noises
significantly reduce the performance of the common EnKF for both linear and
nonlinear systems, whereas our proposed MC-EnKF with a suitable kernel
bandwidth maintains its good performance at only a marginal increase in
computing cost, demonstrating its robustness and efficiency to non-Gaussian
observation noises.Comment: Accepted by 62nd IEEE Conference on Decision and Control (CDC 2023
- …