Non-Negative Matrix Factorization
Non-Negative Matrix Factorization (NMF) is a linear algebra technique that factorizes a non-negative matrix into two lower-rank non-negative matrices. It is commonly used in machine learning and data analysis for dimensionality reduction, feature extraction, and pattern discovery, particularly with data where negative values lack physical meaning (e.g., images, text, audio). NMF enforces non-negativity constraints, leading to parts-based representations that are often more interpretable than other factorization methods.
Developers should learn NMF when working with datasets that have inherent non-negativity, such as in computer vision for image processing, natural language processing for topic modeling, or bioinformatics for gene expression analysis. It is especially useful for tasks requiring interpretable features, like identifying latent topics in documents or extracting facial components from images, as it produces additive combinations of parts rather than subtractive ones.