Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices
arXiv:1402.6964
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 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.
References in corpus (5)
- Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix Factorization
- Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization
- Factoring nonnegative matrices with linear programs
- Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures
- Model Reduction with MapReduce-enabled Tall-and-Skinny Singular Value Decomposition