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#### Kernel Methods for Measuring Independence

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##### Citation

Gretton, A., Herbrich R, Smola A, Bousquet, O., & Schölkopf, B. (2005). Kernel
Methods for Measuring Independence.* Journal of Machine Learning Research,* *6*,
2075-2129. Retrieved from http://jmlr.csail.mit.edu/papers/volume6/gretton05a/gretton05a.pdf.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D367-8

##### Abstract

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 near independence on the Parzen window estimate of the mutual information.
Analogous results apply for two correlation-based dependence functionals introduced earlier: we show the kernel canonical correlation and the kernel generalised variance to be independence measures for universal
kernels, and prove the latter to be an upper bound on the mutual information near independence. The performance of the kernel dependence functionals in measuring independence is verified in the context of independent component analysis.