The mean shift algorithm and its relation to kernel regression

DOI10.1016/j.ins.2016.02.020zbMath1398.62084OpenAlexW2259222853MaRDI QIDQ1991846

Frank Rudzicz, Youness Aliyari Ghassabeh

Publication date: 30 October 2018

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2016.02.020

zbMATH Keywords

asymptotic biasGaussian kernelmean shift algorithmNadaraya-Watson kernel regressionnoisy source vector quantization

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (1)

Novel kernel density estimator based on ensemble unbiased cross-validation

Uses Software

• ElemStatLearn

Cites Work

• A note on the convergence of the mean shift
• On some convergence properties of the subspace constrained mean shift
• Multivariate locally weighted least squares regression
• A distribution-free theory of nonparametric regression
• A sufficient condition for the convergence of the mean shift algorithm with Gaussian kernel
• Asymptotic stability of equilibrium points of mean shift algorithm
• On optimal quantization of noisy sources
• Global convergence of the recursive kernel regression estimates with applications in classification and nonlinear system estimation
• The estimation of the gradient of a density function, with applications in pattern recognition
• Parameter Selection for Principal Curves
• Transmission of noisy information to a noisy receiver with minimum distortion
• Computer Vision - ECCV 2004
• On Estimation of a Probability Density Function and Mode
• The elements of statistical learning. Data mining, inference, and prediction
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: The mean shift algorithm and its relation to kernel regression