article

Sparse canonical correlation analysis

Authors:

David R. Hardoon,

John Shawe-TaylorAuthors Info & Claims

Machine Learning, Volume 83, Issue 3

Pages 331 - 353

https://doi.org/10.1007/s10994-010-5222-7

Published: 01 June 2011 Publication History

Abstract

We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one is interested in (or limited to) a primal representation for the first view while having a dual representation for the second view. Sparse CCA (SCCA) minimises the number of features used in both the primal and dual projections while maximising the correlation between the two views. The method is compared to alternative sparse solutions as well as demonstrated on paired corpuses for mate-retrieval. We are able to observe, in the mate-retrieval, that when the number of the original features is large SCCA outperforms Kernel CCA (KCCA), learning the common semantic space from a sparse set of features.

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Amorosi LPadellini TPuerto JValverde C(2024)A Mathematical Programming Approach to Sparse Canonical Correlation AnalysisExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121293237:PAOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121293
Zhou ZTarzanagh DHou BTong BXu JFeng YLong QShen LOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Fair canonical correlation analysisProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666285(3675-3705)Online publication date: 10-Dec-2023
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Liu WChen YYue XZhang CXie SWilliams BChen YNeville J(2023)Safe multi-view deep classificationProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i7.26066(8870-8878)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i7.26066
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Published In

Machine Language Volume 83, Issue 3

June 2011

153 pages

ISSN:0885-6125

Issue’s Table of Contents

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2011

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Amorosi LPadellini TPuerto JValverde C(2024)A Mathematical Programming Approach to Sparse Canonical Correlation AnalysisExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121293237:PAOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121293
Zhou ZTarzanagh DHou BTong BXu JFeng YLong QShen LOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Fair canonical correlation analysisProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666285(3675-3705)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666285
Liu WChen YYue XZhang CXie SWilliams BChen YNeville J(2023)Safe multi-view deep classificationProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i7.26066(8870-8878)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i7.26066
Sun JXiu XLuo ZLiu W(2023)Learning High-Order Multi-View Representation by New Tensor Canonical Correlation AnalysisIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.326385333:10(5645-5654)Online publication date: 1-Oct-2023
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Watanabe ATamura RTakano YMiyashiro R(2023)Branch-and-bound algorithm for optimal sparse canonical correlation analysisExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119530217:COnline publication date: 1-May-2023
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Yan HCheng LYe QYu DQi Y(2023)Robust generalized canonical correlation analysisApplied Intelligence10.1007/s10489-023-04666-653:18(21140-21155)Online publication date: 17-May-2023
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Guo SSong LXie RLi LLiu S(2022)Multiview nonlinear discriminant structure learning for emotion recognitionKnowledge-Based Systems10.1016/j.knosys.2022.110042258:COnline publication date: 22-Dec-2022
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Shi MLi ZZhao XXu PLiu BGuo J(2022)Trace ratio criterion for multi-view discriminant analysisApplied Intelligence10.1007/s10489-022-03464-w52:13(14679-14692)Online publication date: 1-Oct-2022
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Hussain SKhan MSiddiqi I(2022)Co-clustering based classification of multi-view dataApplied Intelligence10.1007/s10489-021-03087-752:13(14756-14772)Online publication date: 1-Oct-2022
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