Description:

Nonnegative Matrix Factorization is an unsupervised learning method for uncovering latent features in high-dimensional data. This report describes a modifcation of Lin's Projected Gradient Method for NMF which employs a Newton direction in the line search until any constraints become active. Empirical evidence shows that this technique converges faster than existing methods for NMF.

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