The nature of statistical learning theory
The nature of statistical learning theory
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Kernel subspace LDA with optimized kernel parameters on face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Nonlinear adaptive distance metric learning for clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards effective document clustering: A constrained K-means based approach
Information Processing and Management: an International Journal
Applying Electromagnetic Field Theory Concepts to Clustering with Constraints
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Semi-supervised clustering with metric learning: An adaptive kernel method
Pattern Recognition
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Improving constrained clustering with active query selection
Pattern Recognition
A general approach for adaptive kernels in semi-supervised clustering
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Semi-supervised fuzzy clustering with metric learning and entropy regularization
Knowledge-Based Systems
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Semi-supervised clustering uses the limited background knowledge to aid unsupervised clustering algorithms. Recently, a kernel method for semi-supervised clustering has been introduced, which has been shown to outperform previous semi-supervised clustering approaches. However, the setting of the kernel's parameter is left to manual tuning, and the chosen value can largely affect the quality of the results. Thus, the selection of kernel's parameters remains a critical and open problem when only limited supervision, provided in terms of pairwise constraints, is available. In this paper, we derive a new optimization criterion to automatically determine the optimal parameter of an RBF kernel, directly from the data and the given constraints. Our approach integrates the constraints into the clustering objective function, and optimizes the parameter of a Gaussian kernel iteratively during the clustering process. Our experimental comparisons and results with simulated and real data clearly demonstrate the effectiveness and advantages of the proposed algorithm.