Canonical correlation analysis (CCA) is a cornerstone of linear dimensionality reduction techniques that jointly maps two datasets to achieve maximal correlation. CCA has been widely used in applications for capturing data features of interest. In this paper, we establish a range constrained orthogonal CCA (OCCA) model and its variant and apply them for three data analysis tasks of datasets in real-life applications, namely unsupervised feature fusion, multi-target regression and multi-label classification. Numerical experiments show that the OCCA and its variant produce superior accuracy compared to the traditional CCA.

中文翻译：

---

正交典范相关分析与应用

典型相关分析（CCA）是线性降维技术的基石，该技术共同映射两个数据集以实现最大相关性。CCA已广泛用于捕获感兴趣数据特征的应用程序中。在本文中，我们建立了一个范围受限的正交CCA（OCCA）模型及其变体，并将其应用于现实应用中数据集的三个数据分析任务，即无监督特征融合，多目标回归和多标签分类。数值实验表明，与传统的CCA相比，OCCA及其变体产生了更高的精度。