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 by merging a pair of components for each new kernel added. This method is shown to outperform a current state-of-the-art incremental one-class learning algorithm (Incremental SVDD ) on a variety of datasets, while requiring only an upper limit on model complexity to be specified.
|Title of host publication||Artificial Neural Networks - ICANN 2007|
|Editors||Joaquim Marques de Sa, Luis A Alexandre, Wlodzislaw Duch, Danilo Mandic|
|Number of pages||10|
|Publication status||Published - 2007|
|Name||Lecture Notes in Computer Science|
|Publisher||Springer Berlin Heidelberg|