Orthogonal canonical correlation analysis and applications. (3rd July 2020)

• Record Type: Journal Article
• Authors: Wang, Li; Zhang, Lei-hong; Bai, Zhaojun; Li, Ren-Cang
• Is Part Of: Optimization methods and software. Volume 35:Number 4(2020)
• Page Start: 787
• Page End: 807
• Publication Date: 2020-07-03
• Subjects: Canonical correlation analysis (CCA) -- orthogonal CCA -- singular value decomposition -- unsupervised feature fusion -- multi-target regression -- multi-label classification; 15A18 -- 15A21 -- 62H20 -- 62H25 -- 65F15 -- 65F30; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2019.1700257
• Languages: English
• ISSNs: 1055-6788

Abstract

Abstract : 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.