Statistical Change Detection for Multi-Dimensional Data

Posted in Science on August 25, 2008

Statistical Change Detection for Multi-Dimensional Data

This paper deals with detecting change of distribution in multi-dimensional data sets. For a given baseline data set and a set of newly observed data points, we define a statistical test called the density test for deciding if the observed data points are sampled from the underlying distribution that produced the baseline data set. We define a test statistic that is strictly distribution-free under the null hypothesis. Our experimental results show that the density test has substantially more power than the two existing methods for multi-dimensional change detection.

Author: Xiuyao Song, University Of Florida

Watch Video

Tags: Science, Lectures, Computer Science, Data Mining, Machine Learning, VideoLectures.Net, Density estimation