Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Data Mining and Knowledge Discovery
A Consistency-Based Model Selection for One-Class Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions
IEEE Transactions on Computers
A Bayesian approach to bandwidth selection for multivariate kernel density estimation
Computational Statistics & Data Analysis
Finding rare classes: adapting generative and discriminative models in active learning
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
A unifying theory of active discovery and learning
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Active Rare Class Discovery and Classification Using Dirichlet Processes
International Journal of Computer Vision
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An incremental one-class learning algorithm is proposed for the purpose of outlier detection. Outliers are identified by estimating - and thresholding - the probability distribution of the training data. In the early stages of training a non-parametric estimate of the training data distribution is obtained using kernel density estimation. Once the number of training examples reaches the maximum computationally feasible limit for kernel density estimation, we treat the kernel density estimate as a maximally-complex Gaussian mixture model, and keep the model complexity constant bymerging a pair of components for each newkernel added. This method is shown to outperform a current state-of-the-art incremental one-class learning algorithm (Incremental SVDD [5]) on a variety of datasets, while requiring only an upper limit on model complexity to be specified.