On the Consistency of Metric and Non-Metric K-Medoids

He Jiang · Ery Arias-Castro

Keywords: [ Deep Learning ] [ Visualization or Exposition Techniques for Deep Networks ] [ Algorithms; Algorithms ] [ Nonlinear Dimensionality Reduction and Manifold Learning; Deep Learning ] [ Models and Methods ] [ Clustering ]

[ Abstract ]
Wed 14 Apr 12:45 p.m. PDT — 2:45 p.m. PDT


We establish the consistency of K-medoids in the context of metric spaces. We start by proving that K-medoids is asymptotically equivalent to K-means restricted to the support of the underlying distribution under general conditions, including a wide selection of loss functions. This asymptotic equivalence, in turn, enables us to apply the work of Parna (1986) on the consistency of K-means. This general approach applies also to non-metric settings where only an ordering of the dissimilarities is available. We consider two types of ordinal information: one where all quadruple comparisons are available; and one where only triple comparisons are available. We provide some numerical experiments to illustrate our theory.

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