An exhaustive review of adaptive signal processing schemes which are robust, sparsity-
aware and robust as well as sparsity-aware has been carried out in this paper. Conventional …

Correntropy is a local similarity measure defined in kernel space, and the maximum
correntropy criterion (mcc) has been successfully applied in many areas of signal …

As a novel similarity measure that is defined as the expectation of a kernel function between
two random variables, correntropy has been successfully applied in robust machine learning …

According to the minimum error entropy (MEE) criterion in information theory learning (ITL),
the fusion filtering problem of non-Gaussian system is studied in this paper. Combined with …

Due to unavoidable factors, heavy-tailed noise appears in satellite attitude estimation.
Traditional Kalman filter is prone to performance degradation and even filtering divergence …

Sparsity-induced kernel adaptive filters have emerged as a promising candidate for a
nonlinear sparse system identification (SSI) problem. The existing zero-attracting kernel …

The broad learning system (BLS) is an effective machine learning model that exhibits
excellent feature extraction ability and fast training speed. However, the traditional BLS is …

With the emergence of complex correntropy, the maximum complex correntropy criterion
(MCCC) has been applied to the complex-domain adaptive filtering. The MCCC uses the …

Compared with the MSE criterion, the generalized maximum correntropy (GMC) criterion
shows a better robustness against impulsive noise. Some gradient based GMC adaptive …

Recently, the gradient based constrained maximum correntropy criterion (GCMCC)
algorithm has received considerable attention since it provides superior performance to the …