Adaptive Kernel Canonical Correlation Analysis Algorithms for Maximum and Minimum Variance

TitleAdaptive Kernel Canonical Correlation Analysis Algorithms for Maximum and Minimum Variance
Publication TypeConference Paper
Year of Publication2013
AuthorsS. Van Vaerenbergh, J. Vía, J. Manco-Vásquez, and I. Santamaría
Conference NameIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)
Month PublishedMay
Conference LocationVancouver, Canada
AbstractWe describe two formulations of the kernel canonical correlation analysis (KCCA) problem for multiple data sets. The kernel-based algorithms, which allow one to measure nonlinear relationships between the data sets, are obtained as nonlinear extensions of the classical maximum variance (MAXVAR) and minimum variance (MINVAR) canonical correlation analysis (CCA) formulations. We then show how adaptive versions of these algorithms can be obtained by reformulating KCCA as a set of coupled kernel recursive least-squares algorithms. We illustrate the performance of the proposed algorithms on a nonlinear identification application and a cognitive radio detection problem.
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