Non-negative Matrix Factorization (NMF) is a dimensionality reduction technique where a given non-negative matrix is approximated as the product of two lower-rank non-negative matrices, capturing the underlying structure in the data while ensuring interpretability due to the non-negativity constraint. It is widely used in applications like text mining, image processing, and bioinformatics for tasks such as feature extraction and clustering.