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We present a unified view of matrix factorization models, including singular value decompositions, non-negative matrix factorization, probabilistic latent semantic indexing, and generalizations of these models to exponential families and non-regular Bregman divergences. One can model relational data ...

We present a unified view of matrix factorization that frames the differences among popular methods, such as NMF, Weighted SVD, E-PCA, MMMF, pLSI, pLSI-pHITS, Bregman co-clustering, and many others, in terms of a small number of modeling choices. Many of these approaches can be viewed as minimizing ...