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Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices
Austin Benson · Jason D Lee · Bartek Rajwa · David F Gleich

Tue Dec 09 08:40 AM -- 09:00 AM (PST) @ Level 2, room 210

Numerous algorithms are used for nonnegative matrix factorization under the assumption that the matrix is nearly separable. In this paper, we show how to make these algorithms scalable for data matrices that have many more rows than columns, so-called "tall-and-skinny matrices." One key component to these improved methods is an orthogonal matrix transformation that preserves the separability of the NMF problem. Our final methods need to read the data matrix only once and are suitable for streaming, multi-core, and MapReduce architectures. We demonstrate the efficacy of these algorithms on terabyte-sized matrices from scientific computing and bioinformatics.

Author Information

Austin Benson (Cornell University)
Jason D Lee (Stanford)
Bartek Rajwa (Purdue University)
David F Gleich (Purdue University)

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