APPLIED MATH SEMINAR: Archetypal analysis by Professor Braxton Osting (University of Utah)

Archetypal analysis is an unsupervised learning method that uses a convex polytope to summarize multivariate data. For fixed k, the method finds a convex polytope with k vertices, called archetype points, such that the polytope is contained in the convex hull of the data and the mean squared distance between the data and the polytope is minimal. In this talk, I’ll give an overview of the method and discuss connections to matrix factorization, SVD/PCA, and the k-means clustering method. I’ll discuss our recent results proving the consistency of archetypal analysis and describe probabilistic methods for approximate archetypal analysis. This is joint work with Ruijian Han, Dong Wang, Yiming Xu, and Dominique Zosso.