Supervised Fractional-order Embedding Geometrical Multi-view CCA (SFGMCCA) for Multiple Feature Integration

Keisuke Maeda, Yoshiki Ito, Takahiro Ogawa, Miki Haseyama
2020 IEEE Access  
Techniques for integrating different types of multiple features effectively have been actively studied in recent years. Multiset canonical correlation analysis (MCCA), which maximizes the sum of pairwise correlations of inter-view (i.e., between different features), is one of the powerful methods for integrating different types of multiple features, and various MCCA-based methods have been proposed. This work focuses on a supervised MCCA variant in order to construct a novel effective feature
more » ... tegration framework. In this paper, we newly propose supervised fractional-order embedding geometrical multiview CCA (SFGMCCA). This method constructs not only the correlation structure but also two types of geometrical structures of intra-view (i.e., within each feature) and inter-view simultaneously, thereby realizing more precise feature integration. This method also supports the integration of small sample and high-dimensional data by using the fractional-order technique. We conducted experiments using four types of image datasets, i.e., MNIST, COIL-20, ETH-80 and CIFAR-10. Furthermore, we also performed an fMRI dataset containing brain signals to verify the robustness. As a result, it was confirmed that accuracy improvements using SFGMCCA were statistically significant at the significance level of 0.05 compared to those using conventional representative MCCA-based methods. INDEX TERMS Feature integration, multi-view, canonical correlation analysis, fractional-order technique, geometrical structure. 114340 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020
doi:10.1109/access.2020.3003619 fatcat:nyina3vjbrb35lqs6y3a6ts3jm