Ridge-based method for finding curvilinear structures from noisy data

DOI10.1016/j.csda.2014.08.007OpenAlexW2057391934MaRDI QIDQ1623743

Seppo Pulkkinen

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2014.08.007

zbMATH Keywords

predictor-corrector methoddensity estimationfilamentgenerative modelridge curveprincipal curve

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Density estimation (62G07) Applications of statistics to physics (62P35)

Related Items (max. 100)

Nonlinear kernel density principal component analysis with application to climate data ⋮ Multiple penalized principal curves: analysis and computation ⋮ Unnamed Item

Uses Software

• KernSmooth
• pyuvdata

Cites Work

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• Asymptotics for general multivariate kernel density derivative estimators
• Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering
• Generic Structure of Two-Dimensional Images Under Gaussian Blurring
• Principal Curves
• The Geometry of Nonparametric Filament Estimation
• Cross-validation Bandwidth Matrices for Multivariate Kernel Density Estimation
• The Fast Gauss Transform
• Another look at principal curves and surfaces

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