Statistical Convergence of Kernel CCA
Abstract
While kernel canonical correlation analysis (kernel CCA) has been applied in many problems, the asymptotic convergence of the functions estimated from a finite sample to the true functions has not yet been established. This paper gives a rigorous proof of the statistical convergence of kernel CCA and a related method (NOCCO), which provides a theoretical justification for these methods. The result also gives a sufficient condition on the decay of the regularization coefficient in the methods to ensure convergence.
Cite
Text
Fukumizu et al. "Statistical Convergence of Kernel CCA." Neural Information Processing Systems, 2005.Markdown
[Fukumizu et al. "Statistical Convergence of Kernel CCA." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/fukumizu2005neurips-statistical/)BibTeX
@inproceedings{fukumizu2005neurips-statistical,
title = {{Statistical Convergence of Kernel CCA}},
author = {Fukumizu, Kenji and Gretton, Arthur and Bach, Francis R.},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {387-394},
url = {https://mlanthology.org/neurips/2005/fukumizu2005neurips-statistical/}
}