ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 67-71, 2015
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-4-W2/67/2015/
doi:10.5194/isprsannals-II-4-W2-67-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
10 Jul 2015
INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS
A. Bhushan, M. H. Sharker, and H. A. Karimi Geoinformatics Laboratory, School of Information Sciences, University of Pittsburgh, PA15260, USA
Keywords: Outlier Detection, Incremental Principal Component Analysis, Spatiotemporal Data Streams 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.
Conference paper (PDF, 840 KB)


Citation: Bhushan, A., Sharker, M. H., and Karimi, H. A.: INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 67-71, doi:10.5194/isprsannals-II-4-W2-67-2015, 2015.

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