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Discovering true association between multimodal data sets using structured and sparse canonical correlation analysis: A simulation study

Recently, multi-modal data fusion emerged as a comprehensive approach in the field of neuroimaging. As a multivariate approach, canonical correlation analysis (CCA) is frequently used for data fusion. However, the current CCA-based fusion approaches face many problems in the sense of high-dimensionality, multi-colinearity, unimodal feature selection, asymmetric and reshaping of the imaging data into vectors. In this paper, we propose a structured and sparse CCA (ssCCA) technique to overcome the above problems. To investigate the performance of the proposed approach, we compared three multivariate and multi-modal fusion techniques: standard CCA, regularized sparse CCA, and ssCCA, with respect to their ability to detect multi-modal data associations. The results demonstrate that the ssCCA approach has superior recovery performance in the extraction of the true correlated variables relative to the other two fusion approaches.