Linear Algebra for Data Science
Non-negative Matrix Factorization (NMF) is a technique used to factorize a non-negative matrix into two lower-dimensional non-negative matrices, usually referred to as the basis and coefficient matrices. This method is particularly useful in data science for tasks such as feature extraction, dimensionality reduction, and clustering, as it ensures that the resulting factors maintain interpretability, which is often crucial when analyzing real-world data.
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