Sonia Cafieri, Jon Lee, et al.
Journal of Global Optimization
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.
Sonia Cafieri, Jon Lee, et al.
Journal of Global Optimization
M.B. Small, R.M. Potemski
Proceedings of SPIE 1989
Shu Tezuka
WSC 1991
Joy Y. Cheng, Daniel P. Sanders, et al.
SPIE Advanced Lithography 2008