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 integration framework. In this paper, we newly propose supervised fractional-order embedding geometrical multi-view 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.

中文翻译：

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用于多特征集成的监督分数阶嵌入几何多视图 CCA (SFGMCCA)

近年来，人们积极研究了有效整合不同类型的多特征的技术。Multiset canonical correlation analysis (MCCA) 最大化视点间（即不同特征之间）的成对相关性的总和，是整合不同类型多特征的强大方法之一，并且已经提出了各种基于 MCCA 的方法. 这项工作侧重于受监督的 MCCA 变体，以构建一种新颖的有效特征集成框架。在本文中，我们新提出了监督分数阶嵌入几何多视图 CCA (SFGMCCA)。该方法不仅构建了相关结构，而且同时构建了视图内（即每个特征内）和视图间两种几何结构，从而实现了更精确的特征集成。该方法还支持使用分数阶技术来整合小样本和高维数据。我们使用四种类型的图像数据集进行了实验，即 MNIST、COIL-20、ETH-80 和 CIFAR-10。此外，我们还执行了包含大脑信号的 fMRI 数据集以验证稳健性。结果证实，与使用基于 MCCA 的传统代表性方法相比，使用 SGFMCCA 的精度提高在 0.05 的显着性水平上具有统计学意义。