Kernel ellipsoidal trimming
Kernel ellipsoidal trimming
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.
Novelty/outlier detection, optimal experimental design, active learning, kernel methods
University of Southampton
Dolia, A.N.
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Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Shawe-Taylor, J.
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Titterington, D.M.
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October 2005
Dolia, A.N.
5bf0ed58-7341-4147-8d05-8bab99a6d038
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db
Titterington, D.M.
bdfdf43b-7423-4842-9eab-740a5b840e24
Dolia, A.N., Harris, C.J., Shawe-Taylor, J. and Titterington, D.M.
(2005)
Kernel ellipsoidal trimming
(Technical Report Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science, T8.11.10-01/05)
University of Southampton
Record type:
Monograph
(Project Report)
Abstract
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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Published date: October 2005
Keywords:
Novelty/outlier detection, optimal experimental design, active learning, kernel methods
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 263345
URI: http://eprints.soton.ac.uk/id/eprint/263345
PURE UUID: 82b80436-5da4-42af-862c-cc8775392cb8
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Date deposited: 26 Jan 2007
Last modified: 14 Mar 2024 07:30
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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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