Multidimensional Scaling With Very Large Datasets

DOI10.1080/10618600.2018.1470001OpenAlexW2802686662WikidataQ129857178 ScholiaQ129857178MaRDI QIDQ3391174

Emmanuel Paradis

Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://hal.ird.fr/ird-01920130/file/Paradis_2018_JCGS.pdf

zbMATH Keywords

dimension reductionrandom matricesprojection methoddistance data

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (5)

Modeling and visualizing competitiveness in soccer leagues ⋮ Reduced multidimensional scaling ⋮ A Distance-Preserving Matrix Sketch ⋮ ProcData: an R package for process data analysis ⋮ Goodness-of-fit filtering in classical metric multidimensional scaling with large datasets

Uses Software

• R

Cites Work

• Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
• Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
• A Family of Variable-Metric Methods Derived by Variational Means
• A new approach to variable metric algorithms
• The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations
• Conditioning of Quasi-Newton Methods for Function Minimization

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