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INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS for SPATIOTEMPORAL DATA STREAMS

Bhushan, A and Sharker, MH and Karimi, HA (2015) INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS for SPATIOTEMPORAL DATA STREAMS. In: UNSPECIFIED.

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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 (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Bhushan, A
Sharker, MHmhs37@pitt.eduMHS37
Karimi, HAhkarimi@pitt.eduHKARIMI
Date: 10 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
Volume: 2
Number: 4W2
Page Range: 67 - 71
Event Type: Conference
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
ISSN: 2194-9042
Date Deposited: 21 Jun 2017 15:36
Last Modified: 11 Jul 2018 12:55
URI: http://d-scholarship.pitt.edu/id/eprint/32516

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