Computing correlation anomaly scores using stochastic nearest neighbors

Tsuyoshi Idé, Spiros Papadimitriou, Michail Vlachos

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

53 Scopus citations

Abstract

This paper addresses the task of change analysis of correlated multi-sensor systems. The goal of change analysis is to compute the anomaly score of each sensor when we know that the system has some potential difference from a reference state. Examples include validating the proper performance of various car sensors in the automobile industry. We solve this problem based on a neighborhood preservation principle - If the system is working normally, the neighborhood graph of each sensor is almost invariant against the fluctuations of experimental conditions. Here a neighborhood graph is defined based on the correlation between sensor signals. With the notion of stochastic neighborhood, our method is capable of robustly computing the anomaly score of each sensor under conditions that are hard to be detected by other naive methods.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
Pages523-528
Number of pages6
DOIs
StatePublished - 2007
Externally publishedYes
Event7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE, United States
Duration: Oct 28 2007Oct 31 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM

Other

Other7th IEEE International Conference on Data Mining, ICDM 2007
Country/TerritoryUnited States
CityOmaha, NE
Period10/28/0710/31/07

ASJC Scopus subject areas

  • Engineering(all)

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