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Non-Negative Matrix Factorization (NMF) uses techniques from multivariate analysis and linear algebra. NMF decomposes multivariate data by creating a user-defined number of features. Each feature is a linear combination of the original attribute set. The coefficients of these linear combinations are nonnegative.
NMF decomposes a data matrix V into the product of two lower rank matrices W and H so that V is approximately equal to W times H. NMF uses an iterative procedure to modify the initial values of W and H so that the product approaches V. The procedure terminates when the approximation error converges or the specified number of iterations is reached.
When applying to a model, an NMF model maps the original data into the new set of attributes (features) discovered by the model.