Robust regression and outlier detection
Robust regression and outlier detection
A probabilistic resource allocating network for novelty detection
Neural Computation
Local Expert Autoassociators for Anomaly Detection
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Novelty Detection with Multivariate Extreme Value Statistics
Journal of Signal Processing Systems
SOM-based novelty detection using novel data
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Techniques for knowledge acquisition in dynamically changing environments
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
Learning from others: Exchange of classification rules in intelligent distributed systems
Artificial Intelligence
Artificial Intelligence in Medicine
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This paper describes an approach to handle multivariate training data which contain outliers. The aim is to analyze the training patterns and to detect anomalous patterns. Therefore we explicitly model the existence of outliers in the training data using a widespread outlier distribution. Indicator variables assign each pattern to either the outlier distribution or the distribution of normal patterns. Thus we can estimate the data distribution using the EM-algorithm or Data Augmentation. We present the general approach as well as a concrete realization where we use Gaussian mixture models to describe the patterns' distribution. Experimental results show the applicability of this approach for practical studies.