h‐plots for displaying nonmetric dissimilarity matrices

DOI10.1002/sam.11177OpenAlexW2034453764MaRDI QIDQ4969887

Irene Epifanio

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: http://hdl.handle.net/10234/54722

zbMATH Keywords

visualizationembeddingmultidimensional scalingproximity datanon-Euclidean pairwise data

Mathematics Subject Classification ID

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

Related Items (5)

Unnamed Item ⋮ Archetypoids: a new approach to define representative archetypal data ⋮ Archetypal curves in the shape and size space: discovering the salient features of curved big data by representative extremes ⋮ Indefinite Proximity Learning: A Review ⋮ Archetypal Analysis With Missing Data: See All Samples by Looking at a Few Based on Extreme Profiles

Uses Software

• R
• S-PLUS
• MASS (R)
• vegan
• ElemStatLearn
• t-SNE

Cites Work

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• Visualizing non-metric similarities in multiple maps
• The analytical solution of the additive constant problem
• The out-of-sample problem for classical multidimensional scaling
• Modern multidimensional scaling: theory and applications
• Clustering of spatial point patterns
• On the information and representation of non-Euclidean pairwise data
• Statistical Analysis of Financial Data in S-Plus
• The Dissimilarity Representation for Pattern Recognition
• Modern Multivariate Statistical Techniques
• A robust biplot
• Graphical Exploration in Comparing Variance Matrices
• Local Multidimensional Scaling for Nonlinear Dimension Reduction, Graph Drawing, and Proximity Analysis
• The biplot graphic display of matrices with application to principal component analysis

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