Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data

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》资源》论文》Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data

Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data

2020-01-13 |

54 |

45 |

Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data
论文

Abstract

We propose an efficient, generalized, nonparametric, statistical KolmogorovSmirnov test for detecting distributional change in high-dimensional data. To implement the test, we introduce a novel, hierarchical, minimum-volume sets estimator to represent the distributions to be tested. Our work is motivated by the need to detect changes in data streams, and the test is especially efficient in this context. We provide the theoretical foundations of our test and show its superiority over existing methods.

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