Efficient dimensionality reduction for canonical correlation analysis

Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias

Research output: Contribution to conferencePaperpeer-review

25 Scopus citations

Abstract

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 standard 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 less operations than the state-of-the-art exact algorithms.

Original languageEnglish
Pages347-355
Number of pages9
StatePublished - 2013
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
Duration: 16 Jun 201321 Jun 2013

Conference

Conference30th International Conference on Machine Learning, ICML 2013
Country/TerritoryUnited States
CityAtlanta, GA
Period16/06/1321/06/13

Funding

FundersFunder number
Defense Advanced Research Projects Agency

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