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Hypothesis testing using pairwise distances and associated kernels

Abstract:

We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, distances between embeddings of distributions to reproducing kernel Hilbert spaces (RKHS), as established in machine learning. The equivalence holds when energy distances are computed with semimetrics of negative type, in which case a kernel may be defined such that the RKHS d...

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Volume:
2
Pages:
1111-1118
Host title:
Proceedings of the 29th International Conference on Machine Learning, ICML 2012
Publication date:
2012-01-01
Source identifiers:
487756
ISBN:
9781450312851
Pubs id:
pubs:487756
UUID:
uuid:86b137e6-e693-4f20-9b91-9624382593e7
Local pid:
pubs:487756
Deposit date:
2014-11-11

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