A new method for performance analysis in nonlinear dimensionality reduction

DOI10.1002/sam.11445OpenAlexW2998595054WikidataQ126412101 ScholiaQ126412101MaRDI QIDQ4970314

Christopher G. Small, Shojaeddin Chenouri, Jiaxi Liang

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1711.06252

zbMATH Keywords

manifoldprincipal component analysisdimension reductionrank correlationlocal tangent space alignmentmaximum variance unfoldingisomap

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (1)

A technique for characterising feature size and quality of manifolds

Uses Software

• ElemStatLearn

Cites Work

• Self-organized formation of topologically correct feature maps
• Principal component analysis.
• Local Procrustes for manifold embedding: a measure of embedding quality and embedding algorithms
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Local Multidimensional Scaling for Nonlinear Dimension Reduction, Graph Drawing, and Proximity Analysis
• Nonlinear Dimensionality Reduction
• The elements of statistical learning. Data mining, inference, and prediction
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