可扩展的非负矩阵分解方法用于近可分离的高瘦矩阵
Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices
AI总结：
本文提出高效算法处理高瘦矩阵的非负矩阵分解，通过正交变换保持分离性，适用于流式数据和分布式计算环境。
摘要：
许多算法在假设矩阵近可分离的情况下用于非负矩阵分解。本文展示如何使这些算法高效处理行数远多于列数的高瘦矩阵。改进方法的关键是保持NMF问题分离性的正交矩阵变换。最终方法只需单次遍历数据矩阵，适用于流式、多核和MapReduce架构。我们在TB级合成矩阵和科学计算、生物信息学的真实数据上验证了算法的有效性。
英文摘要：
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 efficient 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 a single pass over the data matrix and are suitable for streaming, multi-core, and MapReduce architectures. We demonstrate the efficacy of these algorithms on terabyte-sized synthetic matrices and real-world matrices from scientific computing and bioinformatics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
