Bayesian radial basis functions of variable dimension
Neural Computation
Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes
Computers and Industrial Engineering
A familiarity discrimination algorithm inspired by computations of the perirhinal cortex
Emergent neural computational architectures based on neuroscience
A Mixture Approach to Novelty Detection Using Training Data with Outliers
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Observational Learning with Modular Networks
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
A Familiarity Discrimination Algorithm Inspired by Computations of the Perirhinal Cortex
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Novelty detection: a review—part 1: statistical approaches
Signal Processing
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Facial emotion recognition by adaptive processing of tree structures
Proceedings of the 2006 ACM symposium on Applied computing
IEEE Transactions on Knowledge and Data Engineering
Probabilistic based recursive model for adaptive processing of data structures
Expert Systems with Applications: An International Journal
ACM Computing Surveys (CSUR)
A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes
Data Mining and Knowledge Discovery
A theoretical framework for multi-sphere support vector data description
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Multiple distribution data description learning algorithm for novelty detection
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Probabilistic based recursive model for face recognition
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
A novel parameter refinement approach to one class support vector machine
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
On the pattern recognition and classification of stochastically episodic events
Transactions on Compuational Collective Intelligence VI
L1 norm based KPCA for novelty detection
Pattern Recognition
Identifying anomalous social contexts from mobile proximity data using binomial mixture models
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Pattern Recognition
Review: A review of novelty detection
Signal Processing
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The detection of novel or abnormal input vectors is ofimportance in many monitoring tasks, such as fault detection incomplex systems and detection of abnormal patterns in medicaldiagnostics. We have developed a robust method for noveltydetection, which aims to minimize the number of heuristicallychosen thresholds in the novelty decision process. We achieve thisby growing a gaussian mixture model to form a representation of atraining set of "normal" system states. When previously unseen dataare to be screened for novelty we use the same threshold aswas used during training to define a novelty decision boundary. Weshow on a sample problem of medical signal processing that thismethod is capable of providing robust novelty decision boundariesand apply the technique to the detection of epileptic seizureswithin a data record.