Kernel methods for measuring independence
We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables. These quantities are both based on the covariance between functions of the random variables in reproducing kernel Hilbert spaces (RKHSs). We prove that when the RKHSs are universal, both functionals are zero if and only if the random variables are pairwise independent. We also show that the kernel mutual information is an upper bound...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Machine Learning Research|
|Gretton_Kernel2005.pdf||467.73 kB||Adobe PDF|
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