Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging

• Published in: Frontiers in neuroinformatics Vol. 10; p. 49
• Main Authors: Bilenko, Natalia Y., Gallant, Jack L.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 22.11.2016; Frontiers Media S.A
• Subjects: Analysis of covariance; Bioinformatics; Brain architecture; Brain mapping; canonical correlation analysis; Correlation analysis; covariance analysis; cross-subject alignment; Datasets; Eigenvalues; fMRI; Functional magnetic resonance imaging; Medical imaging; Methods; Multivariate analysis; Neuroimaging; Neuroscience; partial least squares regression; Principal components analysis; python; Registration; Retina; United States > US; California; fMRI; covariance analysis; cross-subject alignment; partial least squares regression; canonical correlation analysis; Python

In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polyn...