Pulsar profile denoising using kernel regression based on maximum correntropy criterion

Yidi Wang; Wei Zheng; Dapeng Zhang; Lu Zhang

doi:10.1016/j.ijleo.2016.10.132

ScienceGate Book Chapters

JOURNAL ARTICLE

Pulsar profile denoising using kernel regression based on maximum correntropy criterion

Yidi Wang Wei Zheng Dapeng Zhang Lu Zhang

Year: 2016 Journal: Optik Vol: 130 Pages: 757-764 Publisher: Elsevier BV

DOI: 10.1016/j.ijleo.2016.10.132

Get Full-Text PDF Get Analytical Report

Keywords:

Kernel (algebra) Noise reduction Computer science Gaussian Regression Pattern recognition (psychology) Artificial intelligence Noise (video) Kernel regression Gaussian noise Gaussian function Regression analysis Algorithm Statistics Mathematics Machine learning Physics

Metrics

Cited By

1.29

FWCI (Field Weighted Citation Impact)

Refs

0.84

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Seismic Imaging and Inversion Techniques

Physical Sciences → Earth and Planetary Sciences → Geophysics

Image and Signal Denoising Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Structural Health Monitoring Techniques

Physical Sciences → Engineering → Civil and Structural Engineering

Pulsar profile denoising using kernel regression based on maximum correntropy criterion

Metrics

Citation History

Topics

Related Documents

Maximum Correntropy Criterion–Based Kernel Adaptive Filters

Maximum correntropy criterion based regression for multivariate calibration

Stochastic Gradient Descent for Kernel-Based Maximum Correntropy Criterion

Online Gradient Descent for Kernel-Based Maximum Correntropy Criterion

Kernel-based maximum correntropy criterion with gradient descent method