SVD Factorization for Tall-and-Fat Matrices on Map/Reduce Architectures
نویسنده
چکیده
We demonstrate an implementation for an approximate rank-k SVD factorization, combiningwell-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× k matrices computed on a single machine, greatly easing the computability of the problem.
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ورودعنوان ژورنال:
- CoRR
دوره abs/1310.4664 شماره
صفحات -
تاریخ انتشار 2013