The nature of statistical learning theory
The nature of statistical learning theory
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Robust pseudo-hierarchical support vector clustering
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
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The internet and a growing number of increasingly sophisticated measuring devices make vast amounts of data available in many applications. However, the dimensionality is often high, and the time available for manual labelling scarce. Methods for unsupervised novelty detection are a great step towards meeting these challenges, and the support vector domain description has already shown its worth in this field. The method has recently received more attention, since it has been shown that the regularization path is piece-wise linear, and can be calculated efficiently. The presented work restates the new findings in a manner which permits the calculation with O(n.n"B) complexity in each iteration step instead of On^2+n"B^3, where n is the number of data points and n"B is the number of boundary points. This is achieved by updating and downdating the system matrix to avoid redundant calculations. We believe this will further promote the use of this method.