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EFFICIENT DIMENSIONALITY REDUCTION FOR CANONICAL CORRELATION ANALYSIS
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DOI：10.1137/130919222.png)
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Abstract
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We present a fast algorithm for approximate canonical correlation analysis (CCA). Given a pair of tall-and-thin matrices, the proposed algorithm first employs a randomized dimensionality reduction transform to reduce the size of the input matrices, and then applies any CCA algorithm to the new pair of matrices. The algorithm computes an approximate CCA to the original pair of matrices with provable guarantees while requiring asymptotically fewer operations than the state-of-the-art exact algorithms.
Keywords:
principal angles
canonical correlations
randomized algorithms
dimensionality reduction
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