The entire regularization path for the support vector domain description

AbstractThe support vector domain description is a one-class classi-
fication method that estimates the shape and extent of the distribution
of a data set. This separates the data into outliers, outside the decision
boundary, and inliers on the inside. The method bears close resemblance
to the two-class support vector machine classifier. Recently, it was shown
that the regularization path of the support vector machine is piecewise
linear, and that the entire path can be computed efficiently. This pa-
per shows that this property carries over to the support vector domain
description. Using our results the solution to the one-class classification
can be solved for any amount of regularization with roughly the same
computational complexity required to solve for a particularly value of
the regularization parameter. The possibility of evaluating the results
for any amount of regularization not only offers more accurate and re-
liable models, but also makes way for new applications. We illustrate
the potential of the method by determining the order of inclusion in the
model for a set of corpora callosa outlines.
Keywordssupport vector domain description, one class classification, support vector machines, piece-wise linear, outlier detection, regularization path
TypeConference paper [With referee]
ConferenceMedical Image Computing and Computer-Assisted Intervention, MICCAI 2006, Copenhagen, Denmark
Year2006    Month October
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
NoteWinner of best student paper award in the category of image analysis
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics