Orthogonal canonical correlation analysis and applications
Li Wang, Lei‐Hong Zhang, Zhaojun Bai, Ren‐Cang Li
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.
Topics & Concepts
Canonical correlationComputer scienceDimensionality reductionPattern recognition (psychology)CorrelationRange (aeronautics)Artificial intelligenceFeature (linguistics)Data miningCurse of dimensionalityPrincipal component analysisMathematicsComposite materialMaterials scienceLinguisticsPhilosophyGeometryFace and Expression RecognitionBlind Source Separation TechniquesRemote Sensing and Land Use