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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Measuring Statistical Dependence with Hilbert-Schmidt Norms
verfasst von : Arthur Gretton, Olivier Bousquet, Alex Smola, Bernhard Schölkopf
Erschienen in: Algorithmic Learning Theory
Verlag: Springer Berlin Heidelberg
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We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.