Overview
Non-negative Matrix Factorization (NMF) is a suite of algorithms used in multivariate analysis and linear algebra to factorize a matrix into two or more matrices, ensuring all elements are non-negative. This non-negativity constraint leads to more interpretable factors. The core idea is to approximate an input matrix V as the product of two smaller matrices, W and H, where V ≈ WH. NMF is particularly useful when the data has inherent non-negativity, such as audio spectrograms or muscular activity measurements. By decomposing data into additive, non-negative parts, NMF can uncover underlying patterns and features. It is commonly applied in areas like document clustering, image processing, bioinformatics, and recommender systems, offering a valuable tool for dimensionality reduction and feature extraction.