  We briefly review the basic ideas behind archetypal analysis for matrix
factorization and discuss its behavior in approximating the convex hull of a
data sample. We then ask how good such approximations can be and consider
different cases. Understanding archetypal analysis as the problem of computing
a convexity constrained low-rank approximation of the identity matrix provides
estimates for archetypal analysis and the SiVM heuristic.
