Kernel Ellipsoidal Trimming
Dolia, A.N., Harris, C.J., Shawe-Taylor, J. and Titterington, D.M. (2005) Kernel Ellipsoidal Trimming.
This is the latest version of this item.
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 (Technical Report)|
|Keywords:||Novelty/outlier detection, optimal experimental design, active learning, kernel methods|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||26 Jan 2007|
|Last Modified:||27 Mar 2014 20:07|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Available Versions of this Item
- Kernel Ellipsoidal Trimming. (deposited 26 Jan 2007) [Currently Displayed]
Actions (login required)