Abstract Archetypal analysis represents a set of observations as convex combinations of pure patterns, or archetypes. The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real-valued. This, unfortunately, is not compatible with many practical situations. In this paper we revisit archetypal analysis from the basic principles, and propose a probabilistic framework that accommodates other observation types such as integers, binary, and probability vectors. We corroborate the proposed methodology with convincing real-world applications on finding archetypal soccer players based on performance data, archetypal winter tourists based on binary survey data, archetypal disaster-affected countries based on disaster count data, and document archetypes based on term-frequency data. We also present an appropriate visualization tool to summarize archetypal analysis solution better.
A probabilistic version of archetypal analysis was introduced by REF , lifting the restriction of archetypal analysis to real-valued data and instead allowing other observation types such as integers, binary, and probability vectors as input.
Probabilistic archetypal analysis
Machine Learning
Machine Learning
Mathematics Computer Science