Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization
Published in SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, 2020
Recommended citation: S. Eswar, K. Hayashi, G. Ballard, R. Kannan, R. Vuduc and H. Park, "Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization," SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, Atlanta, GA, USA, 2020, pp. 1-14, doi: 10.1109/SC41405.2020.00078. http://hayakb95.github.io/files/SymNMF_SC20.pdf
We present two parallel distributed memory algorithms for finding approximate solutions to the Symmetric Nonnegative Matrix Factorization problem. These are the first such algorithms. One is based of the Alternating Nonnegative Least Squares method. The other is based on the Gauss-Newton method with Conjugate Gradients.
Recommended citation: ‘S. Eswar, K. Hayashi, G. Ballard, R. Kannan, R. Vuduc and H. Park, “Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization,” SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, Atlanta, GA, USA, 2020, pp. 1-14, doi: 10.1109/SC41405.2020.00078.’ SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. 1(2).