A Survey on Archetypal Analysis

Abstract:Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure for extracting distinct aspects, so-called archetypes, from observations, with each observational record approximated as a mixture (i.e., convex combination) of these archetypes. AA thereby provides straightforward, interpretable, and explainable representations for feature extraction and dimensionality reduction, facilitating the understanding of the structure of high-dimensional data and enabling wide applications across the sciences. However, AA also faces challenges, particularly as the associated optimization problem is nonconvex. This is the first survey that provides researchers and data mining practitioners with an overview of the methodologies and opportunities that AA offers, surveying the many applications of AA across disparate fields of science, as well as best practices for modeling data with AA and its limitations. The survey concludes by explaining crucial future research directions concerning AA.

Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2504.12392 [stat.ME]

(or arXiv:2504.12392v3 [stat.ME] for this version)

https://doi.org/10.48550/arXiv.2504.12392 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/TPAMI.2026.3740133 Focus to learn more DOI(s) linking to related resources

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