Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
A clustering based feature selection method in spectro-temporal domain for speech recognition
Engineering Applications of Artificial Intelligence
SR-NBS: A fast sparse representation based N-best class selector for robust phoneme classification
Engineering Applications of Artificial Intelligence
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Support vector clustering (SVC) is an important boundary-based clustering algorithm in multi applications. But SVC's popularity is degraded by its pricy computation and poor labeling performance. Different from existing modifications that only resolve one of two bottlenecks, this paper presents an improved SVC, iSVC, to address two bottlenecks simultaneously. iSVC's contributions are as follows: (1) It includes a reduction strategy that can help to develop clustering model on a qualified subset. The reduction strategy is based on the Schrodinger equation to find the crucial data towards model formulation. (2) The original objective is modified; it cooperates with the reduction strategy to produce the model with subtle loss of quality. (3) iSVC employs a new label approach to label data according to the geometric properties of feature space. The new approach labels data in a simple but effective way without suffering from the randomness originated in the old algorithm. (4) The geometric property is proofed to guarantee the new labeling approach's validation. Theoretical analysis and empirical evidence suggest that iSVC overcomes two bottlenecks well. And when compared with some common clustering methods, it does a good job in performance and efficiency, which opens a broad way of applications for SVC.