Efficient and Generalizable Archetypal Analysis for Discrete Data
The paper introduces a new likelihood-based framework called DAAA for Archetypal Analysis (AA) that works efficiently with discrete data like counts or categories, unlike classical methods which struggle with these data types. It uses sequential minimal optimization and local quadratic approximations to find archetypes and includes a cross-validated predictive likelihood criterion to intelligently select the optimal number of archetypes.