Nonnegative Matrix Factorization

Nonnegative Matrix Factorization (NMF) is an unsupervised machine learning technique used to decompose a data matrix into two nonnegative matrices, revealing underlying latent structures and features. Current research emphasizes improving NMF's robustness to noise and outliers through weighted approaches and Bayesian methods incorporating implicit regularizers, as well as extending NMF to higher-dimensional data (tensors) and incorporating concepts like coseparability for efficiency. These advancements enhance NMF's applicability in diverse fields, including dimensionality reduction, single-cell RNA sequencing analysis, signal processing, and recommendation systems, by providing more accurate and interpretable results.