Fundamenta Informaticae 148 (2016) 133–155 133 DOI 10.3233/FI-2016-1427 IOS Press Rough Hypercuboid Based Supervised Regularized Canonical Correlation for Multimodal Data Analysis ∗ Pradipta Maji † and Ankita Mandal Biomedical Imaging and Bioinformatics Lab Machine Intelligence Unit, Indian Statistical Institute 203 B. T. Road, Kolkata, 700 108, West Bengal, India E-mail: {pmaji,amandal}@isical.ac.in. Abstract. One of the main problems in real life omics data analysis is how to extract relevant and non-redundant features from high dimensional multimodal data sets. In general, supervised regularized canonical correlation analysis (SRCCA) plays an important role in extracting new features from multimodal omics data sets. However, the existing SRCCA optimizes regularization parameters based on the quality of first pair of canonical variables only using standard feature evaluation indices. In this regard, this paper introduces a new SRCCA algorithm, integrating judiciously the merits of SRCCA and rough hypercuboid approach, to extract relevant and non- redundant features in approximation spaces from multimodal omics data sets. The proposed method optimizes regularization parameters of the SRCCA based on the quality of a set of pairs of canonical variables using rough hypercuboid approach. While the rough hypercuboid approach provides an efficient way to calculate the degree of dependency of class labels on feature set in approximation spaces, the merit of SRCCA helps in extracting non-redundant features from multimodal data sets. The effectiveness of the proposed approach, along with a comparison with related existing approaches, is demonstrated on several real life data sets. Keywords: Multimodal data analysis, canonical correlation analysis, feature extraction, rough sets, rough hypercuboid approach. * This work is partially supported by the Department of Electronics and Information Technology, Government of India † Address for correspondence: Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700 108, India.