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 property allows for a more interpretable representation of the data, as it can be viewed as a combination of additive components. NMF is widely applied in fields such as natural language processing, image processing, and recommendation systems, enabling the extraction of meaningful patterns and themes from complex datasets.