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Algorithms for two dimensional multi set canonical correlation analysis
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DOI：10.1016/j.patrec.2018.04.038.png)
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Abstract
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Multi set canonical correlation analysis (mCCA), which extends the application of canonical correlation analysis (CCA) to more than two datasets, is a data driven technique that can jointly analyze the relationship amongst multiple (more than two) datasets. However, the conventional mCCA is directly applicable only to multivariate vector data and requires the image data to be reshaped into vectors. This approach fails to consider the spatial structure of the images and in addition, leads to an increase in the computational complexity. In this paper, we propose new two dimensional mCCA algorithms that operate directly on the image data instead of vectorizing them. Face recognition experiments are presented to compare the performances of conventional mCCA and the proposed two dimensional mCCA techniques. Additionally, experiments against fMRI data are conducted to demonstrate the applicability of the proposed approach in multisubject fMRI analysis. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Canonical correlation analysis
Data driven statistical methods
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