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Poster
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices
Austin Benson · Jason D Lee · Bartek Rajwa · David F Gleich
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 (D. E. Shaw Group)
Jason D Lee (University of Southern California)
Bartek Rajwa (Purdue University)
David F Gleich (Purdue University)
Related Events (a corresponding poster, oral, or spotlight)
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2014 Spotlight: Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices »
Tue. Dec 9th 04:40 -- 05:00 PM Room Level 2, room 210
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