  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 time accuracy of L-CCA are established.
The experiments also show that L-CCA outperform other fast CCA approximation
schemes on two real datasets.
