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2006 NIPS 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.

🚀 Conference Pioneer — NIPS 2006
🧭 Keyword Pioneer — two-sample test
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy
📈 Trend Setter — Statistics
🐣 Hot Topic Early Bird — distribution matching

Authors

Arthur Gretton , Karsten Borgwardt , Malte Rasch , Bernhard Schölkopf , Alex J. Smola

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

Keywords

distribution matching statistical testing two-sample test reproducing kernel hilbert space distribution comparison statistical test kernel methods
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