Robust Extraction of Shared and Unique Features from Noisy Multivariate Data

The core message of this article is to propose a principled method called Triple Component Matrix Factorization (TCMF) that can provably separate shared low-rank features, unique low-rank features, and sparse noise from noisy multivariate data, even when the number of parameters to estimate is approximately thrice the number of observations.