hsicCCA-package: Canonical Correlation Analysis based on Kernel Independence...
In hsicCCA: Canonical Correlation Analysis based on Kernel Independence Measures
Description
Details
Author(s)
References
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.
hsicCCA documentation built on May 2, 2019, 7:58 a.m.
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Description Details Author(s) References
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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