In chemometrics non-negative matrix factorization has a long history under the name "self modeling curve resolution".

They are used in matrix factorization, in quantum mechanics, and in many other areas.

This matrix factorization is essentially a problem of linear dimensionality reduction, while Phoenix try to solve it via a distributed way.

This two-way model is related to probabilistic latent semantic analysis and non-negative matrix factorization.

SVD is a matrix factorization that reveals many important properties of a matrix.

Online NMF (Non-negative matrix factorization) is a recently developed method for real time data analysis in an online context.

Non-negative matrix factorization in the past has been used for static data analysis and pattern recognition.

His algorithms for nonnegative matrix factorization have been widely applied to problems in visual learning, semantic analysis, spectroscopy, and bioinformatics.

A particular type of collaborative filtering algorithm uses matrix factorization, a low-rank matrix approximation technique.

A second approach, exemplified by nonnegative matrix factorization, is to impose structural constraints on the source signals.