Negative Matrix Factorization

Non-negative matrix factorization (NMF) is a dimensionality reduction technique used to decompose data into interpretable, non-negative components, primarily aiming to extract meaningful features or latent patterns. Current research focuses on improving NMF's robustness to noise and missing data, optimizing algorithm efficiency (including distributed and parallel implementations), and automating the selection of optimal model parameters. These advancements enhance NMF's applicability across diverse fields, including signal processing, topic modeling, recommendation systems, and medical imaging, by improving accuracy, interpretability, and scalability.