Efficient dimensionality reduction for canonical correlation analysis

Haim Avron*, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)S111-S131
JournalSIAM Journal on Scientific Computing
Issue number5
StatePublished - 2014


FundersFunder number
Seventh Framework Programme259569


    • Canonical correlations
    • Dimensionality reduction
    • Principal angles
    • Randomized algorithms


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