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Non-negative matrix factorization (NMF) is a computational technique used to decompose a non-negative matrix into two lower-dimensional non-negative matrices, often referred to as the basis and coefficients. This method is particularly valuable in applications like text mining and natural language processing because it enables the extraction of latent features from data while ensuring that all components remain non-negative, which aligns well with real-world phenomena such as counts or frequencies.
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