Energy and security are major challenges in a wireless sensor network, and they work
oppositely. As security complexity increases, battery drain will increase. Due to the limited …

Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

Online learning from a signal processing perspective There is increased interest in kernel
learning algorithms in neural networks and a growing need for nonlinear adaptive …

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 …

Correntropy is a local similarity measure defined in kernel space, hence can combat large
outliers in robust signal processing and machine learning. So far, many robust learning …

We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model
that is able to learn a Granger causal graph from a multivariate time series x and …

Kernel adaptive filters have drawn increasing attention due to their advantages such as
universal nonlinear approximation with universal kernels, linearity and convexity in …

This paper discusses an information theoretic approach of designing sparse kernel adaptive
filters. To determine useful data to be learned and remove redundant ones, a subjective …

In this letter, a robust kernel adaptive algorithm, called the kernel recursive maximum
correntropy (KRMC), is derived in kernel space and under the maximum correntropy …

Recently, criterion functions based on information theoretic measures (entropy, mutual
information, information divergence) have attracted attention and become an emerging area …