I come from a CS&Machine Learning discipline. I have been looking to understand the core idea of Non-Negative Matrix Factorization. While most of the ML based work is understandable, mostly the work boils down to theoretical papers. Some examples are:
"On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering"
"SVD based initialization:Ahead start for nonnegative matrix factorization"
However, the papers seem to be quite scary. Do I really have to be a mathematician to understand the following papers? Or if not, which coursework would speed up digging into aforementioned papers?