Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Mixture Approach to Novelty Detection Using Training Data with Outliers
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Gaussian mixture pdf in one-class classification: computing and utilizing confidence values
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Locating emergencies in a campus using wi-fi access point association data
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Detecting rare events using extreme value statistics applied to epileptic convulsions in children
Artificial Intelligence in Medicine
Review: A review of novelty detection
Signal Processing
Extending the Generalised Pareto Distribution for Novelty Detection in High-Dimensional Spaces
Journal of Signal Processing Systems
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Novelty detection, or one-class classification, aims to determine if data are "normal" with respect to some model of normality constructed using examples of normal system behaviour. If that model is composed of generative probability distributions, the extent of "normality" in the data space can be described using Extreme Value Theory (EVT), a branch of statistics concerned with describing the tails of distributions. This paper demonstrates that existing approaches to the use of EVT for novelty detection are appropriate only for univariate, unimodal problems. We generalise the use of EVT for novelty detection to the analysis of data with multivariate, multimodal distributions, allowing a principled approach to the analysis of high-dimensional data to be taken. Examples are provided using vital-sign data obtained from a large clinical study of patients in a high-dependency hospital ward.