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Orthogonal canonical correlation analysis and applications. (English) Zbl 07276054
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.
MSC:
65F15 Numerical computation of eigenvalues and eigenvectors of matrices
15A18 Eigenvalues, singular values, and eigenvectors
90C90 Applications of mathematical programming
Software:
FORS; JDQR; JDQZ; LIBLINEAR
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