As a robust nonlinear similarity measure in kernel space, correntropy has received
increasing attention in domains of machine learning and signal processing. In particular, the …

The steady-state excess mean square error (EMSE) of the adaptive filtering under the
maximum correntropy criterion (MCC) has been studied. For Gaussian noise case, we …

In this paper, we propose a quantization approach, as an alternative of sparsification, to curb
the growth of the radial basis function structure in kernel adaptive filtering. The basic idea …

The applications of error entropy (EE) are sometimes limited because its shape cannot be
flexibly adjusted by the default Gaussian kernel function to adapt to noise variation and thus …

Nonlinear similarity measures defined in kernel space, such as correntropy, can extract
higher order statistics of data and offer potentially significant performance improvement over …

In this paper, a least lncosh (Llncosh) algorithm is derived by utilizing the lncosh cost
function. The lncosh cost is characterized by the natural logarithm of hyperbolic cosine …

We introduce a novel family of adaptive filtering algorithms based on a relative logarithmic
cost inspired by the “competitive methods” from the online learning literature. The …

The hyperbolic cosine function with high-order errors can be utilized to improve the accuracy
of adaptive filters. However, when initial weight errors are large, the hyperbolic cosine …

An adaptive least mean M-estimate algorithm for censored regression (CR-LMM) is
presented for the robust parameter estimation of the censored regression system. To correct …

In the past years, the generalized maximum correntropy criterion (GMCC) has been widely
used in adaptive filters to provide robust behavior under non-Gaussian/impulsive noise …