2006
NeurIPS
NeurIPS 2006
A Kernel Method for the Two-Sample-Problem
Abstract
We propose two statistical tests to determine if two samples are from different dis- tributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS). The first test is based on a large deviation bound for the test statistic, while the second is based on the asymptotic distribution of this statistic. The test statistic can be com- puted in O(m2) time. We apply our approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where our test performs strongly. We also demonstrate excellent performance when compar- ing distributions over graphs, for which no alternative tests currently exist.
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- NeurIPS 2006
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- Estimation
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- Estimation
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Keyword Pioneer
- two-sample test
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Hot Topic Early Bird
- distribution matching
Topics
Machine Learning > Core Methods > Metric Learning
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Learning Types > Supervised Learning
Machine Learning > Optimization & Theory > Statistics
Machine Learning > Core Methods > Kernel Methods
Machine Learning > Core Methods > Estimation