The partial order by inclusion of the principal classes of dissimilarity on a finite set, and some of their basic properties

By Frank Critchley, B. Fichet. 1994

Citations in the corpus

• On the Schoenberg Transformations in Data Analysis: Theory and Illustrations (2010, neutral): “Furthermore, Euclidean distances are arguably more intuitive than kernels, as attested by the development of Geometry and Data Analysis (including their non-Euclidean extensions; see e.g. Critchley and Fichet (1994) for a review).”