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Algorithm Fusion in Novelty Detection

Abstract : Algorithm fusion has received significant attention in the machine learning community in supervised learning mode but it appears little has been done at this point in a novelty detection framework. This paper examines the merit of a fusion strategy wherein metrics from multiple algorithms are treated as entries of a vector whose probability density is subsequently estimated and used for detection. In the present paper the framework is investigated using two algorithms: 1) a robust version of a whiteness test on Kalman filter innovations and 2) a robust version of a scheme that operates with residuals obtained from an orthogonality test. The density estimation part of the process is replaced by the Kernel PCA algorithm which provides a decision boundary without having explicit density estimates. The fused scheme is implemented in a change detection format and is show to provide notable improvements over the use of either algorithm independently.
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https://hal.inria.fr/hal-01021206
Contributor : Anne Jaigu <>
Submitted on : Wednesday, July 9, 2014 - 10:20:25 AM
Last modification on : Wednesday, July 9, 2014 - 3:27:06 PM
Long-term archiving on: : Thursday, October 9, 2014 - 11:22:00 AM

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  • HAL Id : hal-01021206, version 1

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Dionisio Bernal. Algorithm Fusion in Novelty Detection. EWSHM - 7th European Workshop on Structural Health Monitoring, IFFSTTAR, Inria, Université de Nantes, Jul 2014, Nantes, France. ⟨hal-01021206⟩

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