title: Kernel and nonlinear canonical correlation analysis.
abstract: We review a neural implementation of the statistical technique of Canonical Correlation Analysis (CCA) and extend it to nonlinear CCA. We then derive the method of kernel-based CCA and compare these two methods on real and artificial data sets before using both on the Blind Separation of Sources.
authors.affiliation: Applied Computational Intelligence Research Unit, The University of Paisley, Scotland.
authors.forename: P L
authors.initials: PL
authors.lastname: Lai
authors.forename: C
authors.initials: C
authors.lastname: Fyfe
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013223:Statistics as Topic / Q000379:methods*