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• W2098176827 abstract "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." @default.
• W2098176827 created "2016-06-24" @default.
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• W2098176827 date "2014-12-08" @default.
• W2098176827 modified "2023-09-29" @default.
• W2098176827 title "Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices" @default.
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