An overview of Frank-Wolfe optimization for stochasticity constrained interpretable matrix and tensor factorization

In this paper we give an overview about utilizing Frank Wolfe optimization to find interpretable constrained matrix and tensor factorizations. We will particularly concentrate on imposing stochasticity constraints and show how factors of Archetypal Analysis as well as Decomposition Into Directed Components can be found using Frank Wolfe optimization to respectively decompose bipartite matrices and asymmetric similarity tensors. We will show how the derived algorithms perform by presenting case studies from behavioral profiling in digital games.