Kernel Ellipsoidal Trimming
Dolia, A.N., Harris, C.J., Shawe-Taylor, J. and Titterington, D.M. (2005) Kernel Ellipsoidal Trimming s.n.
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Ellipsoid estimation is an issue of primary importance in many practical areas such as control, system identification, visual/audio tracking, experimental design, data mining, robust statistics and novelty/outlier detection. This paper presents a new method of kernel information matrix ellipsoid estimation (KIMEE) that finds an ellipsoid in a kernel defined feature space based on a centered information matrix. Although the method is very general and can be applied to many of the aforementioned problems, the main focus in this paper is the problem of novelty or outlier detection associated with fault detection. A simple iterative algorithm based on Titterington's minimum volume ellipsoid method is proposed for practical implementation. The KIMEE method demonstrates very good performance on a set of real-life and simulated datasets compared with support vector machine methods.
|Item Type:||Monograph (Project Report)|
|Keywords:||Novelty/outlier detection, optimal experimental design, active learning, kernel methods|
|Organisations:||Southampton Wireless Group|
|Date Deposited:||15 Feb 2007|
|Last Modified:||17 Apr 2017 19:52|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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Kernel Ellipsoidal Trimming (deposited 15 Feb 2007)
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