Pattern Recognition Letters
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Recognition of Unconstrained Handwritten Numerals by Doubly Self-Organizing Neural Network
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Kernel PCA for novelty detection
Pattern Recognition
Stacking for Ensembles of Local Experts in Metabonomic Applications
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Selective sampling methods in one-class classification problems
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
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In one-class classification one tries to describe a class of target data and to distinguish it from all other possible outlier objects. Obvious applications are areas where outliers are very diverse or very difficult or expensive to measure, such as in machine diagnostics or in medical applications. In order to have a good distinction between the target objects and the outliers, good representation of the data is essential. The performance of many one-class classifiers critically depends on the scaling of the data and is often harmed by data distributions in (non-linear) subspaces. This paper presents a simple preprocessing method which actively tries to map the data to a spherical symmetric cluster and is almost insensitive to data distributed in subspaces. It uses techniques from Kernel PCA to rescale the data in a kernel feature space to unit variance. This transformed data can now be described very well by the Support Vector Data Description, which basically fits a hypersphere around the data. The paper presents the methods and some preliminary experimental results.