SIAM MDS20 MS13 Advances in Subspace Learning and Clustering Mini-Symposium

Abstract: We study the problem of decomposing a low-rank matrix into a factor with binary entries, either from {±1} or from {0,1}, and an unconstrained factor. This research answers fundamental questions about the existence and uniqueness of these decompositions. It also leads to tractable factorization algorithms that succeed under a mild deterministic condition.