Discussion of a set of points in terms of their mutual distances

By Gale Young, A. Householder. 1938

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Citations in the corpus

Dissimilarity measures in detrital geochronology (2017, methodology): “If the dissimilarity measure fulfils the metric requirements (nonnegativity, symmetry and triangle inequality), then the configuration can be obtained by straightforward matrix algebra (Young and Householder, 1938).”
Comorbidity: A network perspective (2010, background): “Classical (metric) multidimensional scaling (Gower 1966; Young & Householder 1938) requires a matrix representing the pair-wise “distance” between every pair of traits.”
Type 1 and 2 mixtures of Kullback-Leibler divergences as cost functions in dimensionality reduction based on similarity preservation (2013, background): “…b Université catholique de Louvain, Machine Learning Group—ICTEAM, Place du Levant 3, B-1348 Louvain-la-Neuve, Belgium a r t i c l e i n f o Available online 7 March 2013 Keywords: Dimensionality reduction Manifold learning Stochastic neighbor embedding Divergence 12/$ - see front matter &…”
Quality assessment of dimensionality reduction: Rank-based criteria (2009, methodology): “All spectral methods actually turn out to be equivalent to classical metric MDS [ 44 ,35] with a non-Euclidean and/or datadriven metric.”
Sparse multidimensional scaling using land-mark points (2004, background): “Theorem 1 (Young and Iouseholder [16]) Suppose ∆ n is the Euclidean squared-distance matrix for a collection of data points ~y1, ~y2, .”
On the Use of Non-Euclidean Distance Measures in Geostatistics (2007, background): “The following theorem, due originally to Schoenberg (1937), see also Young and Householder (1938) , provides a necessary and sufficient condition for the embedding of a finite metric space.”
Theory of semidefinite programming for Sensor Network Localization (2005, background): “For instance, Schoenberg [29] and Young and Householder [34] have studied the problem in the context of Euclidean distance matrix characterizations.”
Learning to Learn (1998, unknown): “Thorndike & Woodworths (1901) theory of "identical elements" proposed explicitly that the determinant of transfer was the extent to which tasks share identical elements.”
A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data (2015, methodology): “CMDS represents high-dimensional input data in a lower-dimensional output space while preserving pairwise similarities between samples2 (Young and Householder, 1938; Torgerson, 1952).”