A PCA-Based Change Detection Framework for Multidimensional Data Streams

Abdulhakim Ali Ali Qahtan, Basma Mohammed Alharbi, Suojin Wang, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

44 Scopus citations

Abstract

Detecting changes in multidimensional data streams is an important and challenging task. In unsupervised change detection, changes are usually detected by comparing the distribution in a current (test) window with a reference window. It is thus essential to design divergence metrics and density estimators for comparing the data distributions, which are mostly done for univariate data. Detecting changes in multidimensional data streams brings difficulties to the density estimation and comparisons. In this paper, we propose a framework for detecting changes in multidimensional data streams based on principal component analysis, which is used for projecting data into a lower dimensional space, thus facilitating density estimation and change-score calculations. The proposed framework also has advantages over existing approaches by reducing computational costs with an efficient density estimator, promoting the change-score calculation by introducing effective divergence metrics, and by minimizing the efforts required from users on the threshold parameter setting by using the Page-Hinkley test. The evaluation results on synthetic and real data show that our framework outperforms two baseline methods in terms of both detection accuracy and computational costs.
Original languageEnglish (US)
Title of host publicationProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15
PublisherAssociation for Computing Machinery (ACM)
Pages935-944
Number of pages10
ISBN (Print)9781450336642
DOIs
StatePublished - Aug 7 2015

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