NDLI: Improved KPCA for outlier detection in Wireless Sensor Networks

Content Provider	IEEE Xplore Digital Library
Author	Ghorbel, O. Abid, M. Snoussi, H.
Copyright Year	2014
Description	Author affiliation: LM2S Res. Unit, Univ. of Technol. of Troyes, Troyes, France (Snoussi, H.) \|\| Nat. Eng. Sch. of Sfax, Sfax Univ., Sfax, Tunisia (Ghorbel, O.; Abid, M.)
Abstract	In Wireless Sensor Networks (WSNs), the accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. Over the last years, Principal component analysis (PCA) has shown to be a good unsupervised feature extraction. But, this method only focuses on second orders statistics. Recently, Kernel Principal component analysis (KPCA) has used for nonlinear case which can extract higher order statistics. Kernel PCA (KPCA) mapping the data onto another feature space and using nonlinear function. So, we propose an improved KPCA method based on Mahalanobis kernel as a preprocessing step to extract relevant feature for classification and to prevent from the abnormal events. All computation are done in the original space, thus saving computing time using Mahalanobis Kernel (MKPCA). Then the classification was done on real hyperspectral Intel Berkeley data from urban area. Results were positively compared to a version of a standard KPCA specially designed to be use with wireless sensor networks (WSNs).
Sponsorship	ATSI
Starting Page	507
Ending Page	511
File Size	927605
Page Count	5
File Format	PDF
ISBN	9781479948888
DOI	10.1109/ATSIP.2014.6834666
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-17
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Principal Component Analysis (KPCA) Wireless sensor networks Kernel methods Mahalanobis kernel Feature extraction Vectors Data models Eigenvalues and eigenfunctions Outlier Detection Wireless Sensor Networks Kernel Principal component analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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