Non 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 uncover latent structures and features within datasets. Current research focuses on improving NMF's robustness to noise and missing data, addressing its vulnerabilities to adversarial attacks, and enhancing its interpretability through techniques like incorporating constraints, and integrating it with neural networks and other machine learning methods such as autoencoders. NMF finds applications across diverse fields, including audio processing, topic modeling, medical imaging, and community detection, offering valuable tools for data analysis and feature extraction in scenarios where interpretability is crucial.