NDLI: Mixture of designer experts for multi-regime detection in streaming data

Content Provider	IEEE Xplore Digital Library
Author	Kriminger, E. Principe, J. Lakshminarayan, C.
Copyright Year	2012
Description	Author affiliation: University of Florida, Dept. of Electrical and Computer Engineering, Gainesville, FL 32611-6200 (Kriminger, E.; Principe, J.) \|\| HP Labs Palo Alto, California (Lakshminarayan, C.)
Abstract	Real-time streaming data takes on distinct visible patterns, known as regimes, as a result of changing external influences. Regimes corresponding to hazardous states, such as turbulent flow in oil pipelines or patients experiencing heart arrhythmias, must be identified quickly and accurately by on-line detection algorithms. In this paper, we propose a modification to the mixture of experts framework, which is traditionally used to model piecewise stationary time series. Our proposed modification allows experts to produce features specific to their designated regimes, rather than being limited to prediction error. This approach provides the flexibility to update the mixture modularly as new regimes emerge without the burden of retraining the entire mixture, as is typical in traditional classifiers. Our approach is tested on flow rate data from an oil and gas application, as well as detecting heart arrhythmias from electrocardiogram (ECG) signals. It outperforms traditional classification approaches both in terms of error rate and detector delay.
Starting Page	410
Ending Page	414
File Size	110085
Page Count	5
File Format	PDF
ISBN	9781467310680
ISSN	20761465
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-27
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	EURASIP
Subject Keyword	Training Mixture of experts Time series analysis Detectors Electrocardiography Vectors Streaming data Detection Oscillators Delay
Content Type	Text
Resource Type	Article

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