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Kernel Ellipsoidal Trimming

Record type: Monograph (Project Report)

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.

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Citation

Dolia, A.N., Harris, C.J., Shawe-Taylor, J. and Titterington, D.M. (2005) Kernel Ellipsoidal Trimming s.n.

More information

Published date: October 2005
Keywords: Novelty/outlier detection, optimal experimental design, active learning, kernel methods
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 263465
URI: http://eprints.soton.ac.uk/id/eprint/263465
PURE UUID: 830d6d63-8d4c-4090-928a-9b1e03e44c96

Catalogue record

Date deposited: 15 Feb 2007
Last modified: 18 Jul 2017 07:45

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Contributors

Author: A.N. Dolia
Author: C.J. Harris
Author: J. Shawe-Taylor
Author: D.M. Titterington

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