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. NMF decomposes a high-dimensional matrix into two lower-dimensional matrices, ensuring that all values remain non-negative. This characteristic allows for a more interpretable representation of the data, as it can reveal latent topics or features within the dataset. Unlike probabilistic methods, NMF provides a deterministic output, making it a popular choice for applications such as document clustering, image processing, and recommendation systems.