Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications
It serves as a tutorial for researchers and practitioners to understand NMF identifiability, helping them avoid pitfalls and select appropriate tools, but it is incremental as it synthesizes existing knowledge rather than introducing new methods.
This paper addresses the lack of a tutorial on nonnegative matrix factorization (NMF) from an identifiability perspective, providing a comprehensive overview of recent advances in NMF identifiability research and its connections to algorithms and applications.
Nonnegative matrix factorization (NMF) has become a workhorse for signal and data analytics, triggered by its model parsimony and interpretability. Perhaps a bit surprisingly, the understanding to its model identifiability---the major reason behind the interpretability in many applications such as topic mining and hyperspectral imaging---had been rather limited until recent years. Beginning from the 2010s, the identifiability research of NMF has progressed considerably: Many interesting and important results have been discovered by the signal processing (SP) and machine learning (ML) communities. NMF identifiability has a great impact on many aspects in practice, such as ill-posed formulation avoidance and performance-guaranteed algorithm design. On the other hand, there is no tutorial paper that introduces NMF from an identifiability viewpoint. In this paper, we aim at filling this gap by offering a comprehensive and deep tutorial on model identifiability of NMF as well as the connections to algorithms and applications. This tutorial will help researchers and graduate students grasp the essence and insights of NMF, thereby avoiding typical `pitfalls' that are often times due to unidentifiable NMF formulations. This paper will also help practitioners pick/design suitable factorization tools for their own problems.