Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices
复制标题
DOI：
--
复制
发表时间：
2014-02
期刊：
影响因子：
--
通讯作者：
Austin R. Benson;J. Lee;Bartek Rajwa;D. Gleich
中科院分区：
文献类型：
--
作者：
Austin R. Benson;J. Lee;Bartek Rajwa;D. 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.
