Archetypoids: A new approach to define representative archetypal data

Guillermo Vinué, Irene Epifanio and Sandra Alemany

Computational Statistics & Data Analysis, 2015, vol. 87, issue C, 102-115

Abstract: The new concept archetypoids is introduced. Archetypoid analysis represents each observation in a dataset as a mixture of actual observations in the dataset, which are pure type or archetypoids. Unlike archetype analysis, archetypoids are real observations, not a mixture of observations. This is relevant when existing archetypal observations are needed, rather than fictitious ones. An algorithm is proposed to find them and some of their theoretical properties are introduced. It is also shown how they can be obtained when only dissimilarities between observations are known (features are unavailable). Archetypoid analysis is illustrated in two design problems and several examples, comparing them with the archetypes, the nearest observations to them and other unsupervised methods.

Keywords: Archetype; Convex hull; Unsupervised learning; Extremal point; Non-negative matrix factorization (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:87:y:2015:i:c:p:102-115

DOI: 10.1016/j.csda.2015.01.018

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