UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Kernal Ellipsoid Trimming

Dolia, A; Harris, C; Shawe-Taylor, J; Titterington, M; (2007) Kernal Ellipsoid Trimming. Computational Statistics and Data Analysis , 52 (1) pp. 309-324.

Full text not available from this repository.


Ellipsoid estimation is important in many practical areas such as control, system identification, visual/audio tracking, experimental design, data mining, robust statistics and statistical outlier or novelty detection. A new method, called kernel minimum volume covering ellipsoid (KMVCE) estimation, that finds an ellipsoid in a kernel-defined feature space is presented. Although the method is very general and can be applied to many of the aforementioned problems, the main focus is on the problem of statistical novelty/outlier detection. A simple iterative algorithm based on Mahalanobis-type distances in the kernel-defined feature space is proposed for practical implementation. The probability that a non-outlier is misidentified by our algorithms is analysed using bounds based on Rademacher complexity. The KMVCE method performs very well on a set of real-life and simulated datasets, when compared with standard kernel-based novelty detection methods.

Type: Article
Title: Kernal Ellipsoid Trimming
Keywords: minimum volume covering ellipsoid, rademacher complexity, kernel methods, outlier detection, novelty detection
UCL classification: UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/79162
Downloads since deposit
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item