Large Scale Canonical Correlation Analysis with Iterative Least Squares

@inproceedings{Lu2014LargeSC,
  title={Large Scale Canonical Correlation Analysis with Iterative Least Squares},
  author={Yichao Lu and Dean P. Foster},
  booktitle={NIPS},
  year={2014}
}
Canonical Correlation Analysis (CCA) is a widely used statistical tool with both well established theory and favorable performance for a wide range of machine learning problems. However, computing CCA for huge datasets can be very slow since it involves implementing QR decomposition or singular value decomposition of huge matrices. In this paper we introduce L-CCA , a iterative algorithm which can compute CCA fast on huge sparse datasets. Theory on both the asymptotic convergence and finite… CONTINUE READING
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