Non-Negative Matrix Factorization (NMF) is a powerful linear algebra technique used for dimensionality reduction and topic modeling in various data types, particularly in text and image data. By decomposing a high-dimensional matrix into two lower-dimensional matrices, NMF allows for the extraction of latent topics or features while ensuring that all values remain non-negative. This characteristic makes NMF particularly interpretable, as it can reveal meaningful patterns within the data. Unlike probabilistic methods, NMF provides a deterministic output, making it a popular choice for applications such as document clustering, recommendation systems, and image processing.