Wang, Li; Zhang, Lei-hong; Bai, Zhaojun; Li, Ren-Cang Orthogonal canonical correlation analysis and applications. (English) Zbl 1455.65059 Optim. Methods Softw. 35, No. 4, 787-807 (2020). Summary: 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. Cited in 3 Documents MSC: 65F15 Numerical computation of eigenvalues and eigenvectors of matrices 15A18 Eigenvalues, singular values, and eigenvectors 65K05 Numerical mathematical programming methods 90C90 Applications of mathematical programming Keywords:canonical correlation analysis; singular value decomposition; unsupervised feature fusion; multi-target regression; multi-label classification Software:JDQR; FORS; LIBLINEAR; JDQZ PDFBibTeX XMLCite \textit{L. Wang} et al., Optim. Methods Softw. 35, No. 4, 787--807 (2020; Zbl 1455.65059) Full Text: DOI References: [1] Absil, P.-A.; Mahony, R.; Sepulchre, R., Optimization Algorithms On Matrix Manifolds (2008), Princeton University Press: Princeton University Press, Princeton, NJ · Zbl 1147.65043 [2] Bai, Z.; Demmel, J.; Dongarra, J.; Ruhe, A.; van der Vorst, H., Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide (2000), SIAM: SIAM, Philadelphia, PA · Zbl 0965.65058 [3] Chu, D.; Liao, L.; Ng, M. 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