Generalized correntropy for robust adaptive filtering
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 …
increasing attention in domains of machine learning and signal processing. In particular, the …
Mixture correntropy for robust learning
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 …
outliers in robust signal processing and machine learning. So far, many robust learning …
IA-LSTM: interaction-aware LSTM for pedestrian trajectory prediction
Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or
autonomous mobile robot field because estimating the future locations of pedestrians …
autonomous mobile robot field because estimating the future locations of pedestrians …
Multikernel correntropy for robust learning
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 …
two random variables, correntropy has been successfully applied in robust machine learning …
Regularized correntropy criterion based semi-supervised ELM
Along with the explosive growing of data, semi-supervised learning attracts increasing
attention in the past years due to its powerful capability in labeling unlabeled data and …
attention in the past years due to its powerful capability in labeling unlabeled data and …
Robustness of convolutional neural network models in hyperspectral noisy datasets with loss functions
The presence of noise in images affects the classification performance of convolutional
neural networks (CNNs). The loss function plays an important role in the noise robustness of …
neural networks (CNNs). The loss function plays an important role in the noise robustness of …
On the optimization properties of the correntropic loss function in data analysis
Similarity measures play a critical role in the solution quality of data analysis methods.
Outliers or noise often taint the solution, hence, practical data analysis calls for robust …
Outliers or noise often taint the solution, hence, practical data analysis calls for robust …
Estimation of large covariance and precision matrices from temporally dependent observations
We consider the estimation of large covariance and precision matrices from high-
dimensional sub-Gaussian or heavier-tailed observations with slowly decaying temporal …
dimensional sub-Gaussian or heavier-tailed observations with slowly decaying temporal …
Generalized mixed-norm maximum correntropy for robust adaptive filtering
G Li, H Zhang, S Wang, G Wang, J Zhao - Applied Acoustics, 2025 - Elsevier
In robust regression and filtering, the generalized maximum correntropy criterion (GMCC)
has recently been successfully applied. The kernel function in correntropy is the generalized …
has recently been successfully applied. The kernel function in correntropy is the generalized …
Invexity of the minimum error entropy criterion
In this letter, optimization properties of Minimization of Error Entropy (MEE) and Minimization
of Error Entropy with Fiducial points (MEEF) are presented. It is proved that by varying the …
of Error Entropy with Fiducial points (MEEF) are presented. It is proved that by varying the …