Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Outlier Detection Using Classifier Instability
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Support Vector Data Description
Machine Learning
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
A Neural Network-Based Novelty Detector for Image Sequence Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of LVQ to novelty detection using outlier training data
Pattern Recognition Letters
Kernel PCA for novelty detection
Pattern Recognition
Non-parametric statistical background modeling for efficient foreground region detection
Machine Vision and Applications
SOM-based novelty detection using novel data
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Class of Single-Class Minimax Probability Machines for Novelty Detection
IEEE Transactions on Neural Networks
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In this paper, a novelty detection method based on self-organizing map (SOM) and local minimum enclosing spheres is proposed. There are two phases in the proposed approach. In the first phase, the whole training set are split into disjointed Voronoi regions by SOM. In the second phase, several local minimum enclosing spheres are constructed upon these Voronoi regions. Compared with its related works, the proposed method demonstrates better performances on one synthetic data set and two benchmark data sets.