Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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IEEE Transactions on Neural Networks
One-Class Classification by Combining Density and Class Probability Estimation
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ACM Computing Surveys (CSUR)
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IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
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Pattern Recognition
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Pattern Recognition Letters
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The problem of detecting atypical objects or outliers is one of the classical topics in (robust) statistics. Recently, it has been proposed to address this problem by means of one-class SVM classifiers. The method presented in this letter bridges the gap between kernelized one-class classification and gaussian density estimation in the induced feature space. Having established the exact relation between the two concepts, it is now possible to identify atypical objects by quantifying their deviations from the gaussian model. This model-based formalization of outliers overcomes the main conceptual shortcoming of most one-class approaches, which, in a strict sense, are unable to detect outliers, since the expected fraction of outliers has to be specified in advance. In order to overcome the inherent model selection problem of unsupervised kernel methods, a cross-validated likelihood criterion for selecting all free model parameters is applied. Experiments for detecting atypical objects in image databases effectively demonstrate the applicability of the proposed method in real-world scenarios.