Broad learning system based on maximum correntropy criterion
As an effective and efficient discriminative learning method, broad learning system (BLS)
has received increasing attention due to its outstanding performance in various regression …
has received increasing attention due to its outstanding performance in various regression …
Quantized kernel Lleast lncosh algorithm
This paper introduces the kernel least lncosh (KLL) algorithm, in which the lncosh (logarithm
of hyperbolic cosine) cost function is successfully applied in the reproducing-kernel-Hilbert …
of hyperbolic cosine) cost function is successfully applied in the reproducing-kernel-Hilbert …
An improved robust kernel adaptive filtering method for time series prediction
L Shi, R Lu, Z Liu, J Yin, Y Chen, J Wang… - IEEE Sensors …, 2023 - ieeexplore.ieee.org
Time-series prediction is a popular application that relies on the collection of historical data
via sensors, which is then leveraged by predictive models to forecast future values or trends …
via sensors, which is then leveraged by predictive models to forecast future values or trends …
Kernel correntropy conjugate gradient algorithms based on half-quadratic optimization
As a nonlinear similarity measure defined in the kernel space, the correntropic loss (C-Loss)
can address the stability issues of second-order similarity measures thanks to its ability to …
can address the stability issues of second-order similarity measures thanks to its ability to …
Kernel recursive generalized maximum correntropy
J Zhao, H Zhang - IEEE Signal Processing Letters, 2017 - ieeexplore.ieee.org
In this letter, a novel kernel adaptive algorithm, called kernel recursive generalized
maximum correntropy algorithm (KRGMC), is derived in a kernel space and under the …
maximum correntropy algorithm (KRGMC), is derived in a kernel space and under the …
Robust kernel adaptive filtering for nonlinear time series prediction
Recently, online learning algorithms in machine learning have been imposed much
attention. As a typical family, kernel adaptive filtering algorithms receive particular interest …
attention. As a typical family, kernel adaptive filtering algorithms receive particular interest …
Graph diffusion kernel maximum correntropy criterion over sensor network and its performance analysis
X Hou, H Zhao, X Long - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
In recent years, graph signal processing (GSP) has attracted much attention due to its ability
to model irregular and interactive data generated by wireless sensor networks (WSNs) …
to model irregular and interactive data generated by wireless sensor networks (WSNs) …
A quantized kernel learning algorithm using a minimum kernel risk-sensitive loss criterion and bilateral gradient technique
Recently, inspired by correntropy, kernel risk-sensitive loss (KRSL) has emerged as a novel
nonlinear similarity measure defined in kernel space, which achieves a better computing …
nonlinear similarity measure defined in kernel space, which achieves a better computing …
Quantized generalized maximum correntropy criterion based kernel recursive least squares for online time series prediction
T Shen, W Ren, M Han - Engineering Applications of Artificial Intelligence, 2020 - Elsevier
With the rapid development of information theoretic learning, the maximum correntropy
criterion (MCC) has been widely used in time series prediction area. Especially, the kernel …
criterion (MCC) has been widely used in time series prediction area. Especially, the kernel …
Improved proportionate-type sparse adaptive filtering under maximum correntropy criterion in impulsive noise environments
VC Gogineni, S Mula - Digital Signal Processing, 2018 - Elsevier
An improved proportionate adaptive filter based on maximum correntropy criterion (IP-MCC)
is proposed for identifying systems with variable sparsity in an impulsive noise environment …
is proposed for identifying systems with variable sparsity in an impulsive noise environment …