Canonical correlation analysis based on robust covariance matrix by using deterministic of minimum covariance determinant

Canonical correlation analysis (CCA) study the linear combinations between a two multivariate set of variable that have the maximum association among these two sets of variables. The main computation of the CCA is depend on the sample mean and covariance matrices, where they are very sensitive and h...

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Bibliographic Details
Published in:Partial differential equations in applied mathematics : a spin-off of Applied Mathematics Letters 2024-09, Vol.11, p.100820, Article 100820
Main Authors: Alrawashdeh, Mufda Jameel, Saad, Sofian A.A., Mohammed, Abdelrahman Musa Ali, Alrawashdeh, Waad J.A.
Format: Article
Language:English
Subjects:
Canonical correlation analysis
Covariance matrices
DetMCD
Robust estimation
Statistical model
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Summary:Canonical correlation analysis (CCA) study the linear combinations between a two multivariate set of variable that have the maximum association among these two sets of variables. The main computation of the CCA is depend on the sample mean and covariance matrices, where they are very sensitive and highly effected by presence of outliers. In this study a new procedure is used on CCA to obtain the robust canonical correlation analysis (RCCA) to control and conquer the deformities of the sample mean and covariance matrices in contaminated data set. The deterministic of minimum covariance determinant (DetMCD) of Mia et al. (1936) is applied on CCA and the superiority performance of RCCA is demonstrated CCA method. A simulation is running for both methods on real and generation data.
ISSN:2666-8181
2666-8181
DOI:10.1016/j.padiff.2024.100820