Efficient Dimensionality Reduction for Canonical Correlation Analysis

Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(1):347-355, 2013.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v28-avron13, title = {Efficient Dimensionality Reduction for Canonical Correlation Analysis}, author = {Avron, Haim and Boutsidis, Christos and Toledo, Sivan and Zouzias, Anastasios}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {347--355}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {1}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/avron13.pdf}, url = {https://proceedings.mlr.press/v28/avron13.html}, 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. } }
Endnote
%0 Conference Paper %T Efficient Dimensionality Reduction for Canonical Correlation Analysis %A Haim Avron %A Christos Boutsidis %A Sivan Toledo %A Anastasios Zouzias %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-avron13 %I PMLR %P 347--355 %U https://proceedings.mlr.press/v28/avron13.html %V 28 %N 1 %X 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.
RIS
TY - CPAPER TI - Efficient Dimensionality Reduction for Canonical Correlation Analysis AU - Haim Avron AU - Christos Boutsidis AU - Sivan Toledo AU - Anastasios Zouzias BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/02/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-avron13 PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 1 SP - 347 EP - 355 L1 - http://proceedings.mlr.press/v28/avron13.pdf UR - https://proceedings.mlr.press/v28/avron13.html AB - 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. ER -
APA
Avron, H., Boutsidis, C., Toledo, S. & Zouzias, A.. (2013). Efficient Dimensionality Reduction for Canonical Correlation Analysis. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(1):347-355 Available from https://proceedings.mlr.press/v28/avron13.html.

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