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Incremental Principal Component Analysis Based Outliers Detection Methods for Spatiotemporal Data Streams

Bhushan, Alka and Sharker, Monir and Karimi, Hassan A. (2015) Incremental Principal Component Analysis Based Outliers Detection Methods for Spatiotemporal Data Streams. In: The First International Symposium on Spatiotemporal Computing, 13-15 Jul 2015, Fairfax, VA.

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Abstract

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

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Details

Item Type: Conference or Workshop Item (Paper)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Bhushan, Alkaalka.bhushan@yahoo.com
Sharker, Monirmhs37@pitt.eduMHS37
Karimi, Hassan A.hkarimi@pitt.eduHKARIMI
Date: July 2015
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Event Title: The First International Symposium on Spatiotemporal Computing
Event Dates: 13-15 Jul 2015
Event Type: Workshop
DOI or Unique Handle: 10.5194/isprsannals-ii-4-w2-67-2015
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Projects: Hkarimi@pitt.edu
Article Type: Research Article
Date Deposited: 21 Jun 2017 15:37
Last Modified: 28 Sep 2017 15:39
URI: http://d-scholarship.pitt.edu/id/eprint/32514

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  • Incremental Principal Component Analysis Based Outliers Detection Methods for Spatiotemporal Data Streams. (deposited 21 Jun 2017 15:37) [Currently Displayed]

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