A Randomized Algorithm for CCA

Abstract

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.

References in corpus (2)

• Randomized Nonlinear Component Analysis
• Correlated random features for fast semi-supervised learning

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