  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.

Machine Learning;Machine Learning