SVD Factorization for Tall-and-Fat Matrices on Map/Reduce Architectures

25 Oct 2017  ·  Bayramli Burak ·

We demonstrate an implementation for an approximate rank-k SVD factorization, combining well-known randomized projection techniques with previously implemented map/reduce solutions in order to compute steps of the random projection based SVD procedure, such QR and SVD. We structure the problem in a way that it reduces to Cholesky and SVD factorizations on $k \times k$ matrices computed on a single machine, greatly easing the computability of the problem...

PDF Abstract
No code implementations yet. Submit your code now


Distributed, Parallel, and Cluster Computing


  Add Datasets introduced or used in this paper