Non-negative Matrix Factorization

Non-negative Matrix Factorization produces meaningful components especially in data spaces where all values are zero or positive. It can extract interpretable latent structures in tasks such as topic modeling, image decomposition, and biological data analysis. Because components are constrained to be non-negative, the resulting representations are often more naturally interpretable. For that reason, NMF is valuable not only for compression, but also for explainable representation learning.