article

Robust canonical correlations: A comparative study

Authors:

J. A. Branco,

C. Croux,

P. Filzmoser, and

M. R. OliveiraAuthors Info & Claims

Computational Statistics, Volume 20, Issue 2

Pages 203 - 229

https://doi.org/10.1007/BF02789700

Published: 01 June 2005 Publication History

Abstract

Several approaches for robust canonical correlation analysis will be presented and discussed. A first method is based on the definition of canonical correlation analysis as looking for linear combinations of two sets of variables having maximal (robust) correlation. A second method is based on alternating robust regressions. These methods are discussed in detail and compared with the more traditional approach to robust canonical correlation via covariance matrix estimates. A simulation study compares the performance of the different estimators under several kinds of sampling schemes. Robustness is studied as well by breakdown plots.

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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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Published In

Computational Statistics Volume 20, Issue 2

June 2005

168 pages

ISSN:0943-4062

EISSN:1613-9658

Issue’s Table of Contents

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2005

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Luo LWang WBao SPeng XPeng Y(2024)Robust and sparse canonical correlation analysis for fault detection and diagnosis using training data with outliersExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121434236:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121434
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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Aïssa-El-Bey ASeghouane A(undefined)Sparse canonical correlation analysis based on rank-1 matrix approximation and its application for FMRI signals2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2016.7472564(4678-4682)
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Robust dimension reduction based on canonical correlation

General canonical correlations with applications to group symmetry models

Asymptotic properties of canonical correlation analysis for one group with additional observations

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