Algorithms for clustering data
Algorithms for clustering data
The nature of statistical learning theory
The nature of statistical learning theory
Self-organizing maps
Deterministic annealing EM algorithm
Neural Networks
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Stacking for Ensembles of Local Experts in Metabonomic Applications
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Impact of multiple clusters on neural classification of ROIs in digital mammograms
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Information Sciences: an International Journal
An entropy weighting mixture model for subspace clustering of high-dimensional data
Pattern Recognition Letters
Cloosting: clustering data with boosting
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Applying a novel decision rule to the semi-supervised clustering method based on one-class SVM
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Position regularized Support Vector Domain Description
Pattern Recognition
Robust novelty detection in the framework of a contamination neighbourhood
International Journal of Intelligent Information and Database Systems
Robust novelty detection in the framework of a contamination neighbourhood
International Journal of Intelligent Information and Database Systems
Approximate polytope ensemble for one-class classification
Pattern Recognition
Clustering-based ensembles for one-class classification
Information Sciences: an International Journal
| Hi-index | 0.01 |
This paper describes a new soft clustering algorithm in which each cluster is modelled by a one-class support vector machine (OC-SVM). The proposed algorithm extends a previously proposed hard clustering algorithm, also based on OC-SVM representation of clusters. The key building block of our method is the weighted OC-SVM (WOC-SVM), a novel tool introduced in this paper, based on which an expectation-maximization-type soft clustering algorithm is defined. A deterministic annealing version of the algorithm is also introduced, and shown to improve the robustness with respect to initialization. Experimental results show that the proposed soft clustering algorithm outperforms its hard clustering counterpart, namely in terms of robustness with respect to initialization, as well as several other state-of-the-art methods.