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A Randomized Algorithm for CCA
lib:824dd2d1b4b15b5c (v1.0.0)
Authors: Paul Mineiro,Nikos Karampatziakis
ArXiv: 1411.3409
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1411.3409v1
We present RandomizedCCA, a randomized algorithm for computing canonical analysis, suitable for large datasets stored either out of core or on a distributed file system. Accurate results can be obtained in as few as two data passes, which is relevant for distributed processing frameworks in which iteration is expensive (e.g., Hadoop). The strategy also provides an excellent initializer for standard iterative solutions.
ArXiv: 1411.3409
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1411.3409v1
We present RandomizedCCA, a randomized algorithm for computing canonical analysis, suitable for large datasets stored either out of core or on a distributed file system. Accurate results can be obtained in as few as two data passes, which is relevant for distributed processing frameworks in which iteration is expensive (e.g., Hadoop). The strategy also provides an excellent initializer for standard iterative solutions.
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