NMF

Non-Negative Matrix Factorization (NMF) is a powerful technique used in data analysis and machine learning for topic modeling and dimensionality reduction. It decomposes a given non-negative matrix into two lower-dimensional non-negative matrices, allowing for the extraction of meaningful patterns and themes from complex datasets. NMF is particularly effective in applications such as text mining, where it identifies latent topics within a corpus of documents by analyzing word frequency distributions. This method is advantageous because it provides interpretable results, making it easier to understand the underlying structure of the data while maintaining the non-negativity constraint.