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 …

Traditional patch-based sparse representation modeling of natural images usually suffer
from two problems. First, it has to solve a large-scale optimization problem with high …

In this paper, we propose an image super-resolution approach using a novel generic image
prior-gradient profile prior, which is a parametric prior describing the shape and the …

We present a statistical view of the texture retrieval problem by combining the two related
tasks, namely feature extraction (FE) and similarity measurement (SM), into a joint modeling …

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 …

A subband decomposition scheme for video signals, in which the original or difference
frames are each decomposed into 16 equal-size frequency subbands, is considered …

In this paper, we propose a novel generic image prior-gradient profile prior, which implies
the prior knowledge of natural image gradients. In this prior, the image gradients are …

A complete overview of distant automatic speech recognition The performance of
conventional Automatic Speech Recognition (ASR) systems degrades dramatically as soon …

Currently, the generalized maximum correntropy criterion (GMCC) is extensively used in
adaptive filtering arithmetic to handle generalized Gaussian noise. However, when the input …

We develop robust methods for subset selection based on the minimization of diversity
measures. A Bayesian framework is used to account for noise in the data and a maximum a …