Canonical Correlation Analysis based on Kernel Independence Measures

Description

Canonical correlation analysis that extracts nonlinear correlation through the use of Hilbert Schmidt Independence Criterion and Centered Kernel Target Alignment.

Details

Package: hsicCCA
Type: Package
Version: 1.0
Date: 2013-03-13
License: GPL-2

Author(s)

Billy Chang: <billy.chang@mail.utoronto.ca>

References

Chang et. al. (2013) Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment. ICML 2013.

Gretton et. al. (2005) Measuring statistical dependence with Hilbert-Schmidt Norm. In Algorithmic Learning Theory 2005.

Cortes et. al. (2012) Algorithms for learning kernels based on centered alignments. JMLR 13:795-828.