A scale-based approach to finding effective dimensionality in manifold learning

DOI10.1214/07-EJS137zbMath1320.62115arXiv0710.5349MaRDI QIDQ2426829

Xiao-Hui Wang, James Stephen Marron

Publication date: 14 May 2008

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0710.5349

Mathematics Subject Classification ID

Related Items (9)

Intrinsic dimension estimation based on local adjacency information ⋮ Ricci curvature and the manifold learning problem ⋮ Overview of object oriented data analysis ⋮ Understanding autoencoders with information theoretic concepts ⋮ Statistical Scale Space Methods ⋮ Barycentric subspace analysis on manifolds ⋮ Intrinsic dimension estimation: advances and open problems ⋮ Intrinsic Dimensionality Estimation of High-Dimension, Low Sample Size Data withD-Asymptotics ⋮ Prequential analysis of complex data with adaptive model reselection

Uses Software

• fda (R)
• SiZer

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Computer vision and Mathematica
• Measuring the strangeness of strange attractors
• Representation of structure in similarity data: problems and prospects
• Applied functional data analysis. Methods and case studies
• Application of local linear embedding to nonlinear exploratory latent structure analysis
• A Brief Survey of Bandwidth Selection for Density Estimation
• Adaptive Principal Surfaces
• SiZer for Exploration of Structures in Curves
• Principal Curves
• Geodesic Entropic Graphs for Dimension and Entropy Estimation in Manifold Learning
• Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
• An Algorithm for Finding Intrinsic Dimensionality of Data

This page was built for publication: A scale-based approach to finding effective dimensionality in manifold learning