An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Convex Optimization
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
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
Geometry-aware metric learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Constraint projections for ensemble learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Knowledge driven dimension reduction for clustering
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Semi-supervised metric learning using pairwise constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Bagging Constraint Score for feature selection with pairwise constraints
Pattern Recognition
| Hi-index | 0.01 |
Pairwise constraints known as must-link and cannot-link constraints have been frequently used in semi-supervised clustering. In this paper, we propose a novel usage of cannot-link constraints and develop a method called Mid-Perpendicular Hyperplane Similarity (MPHS) for semi-supervised clustering. Since a cannot-link constraint means that the two objects linked by it are not in the same class, there is a mid-perpendicular hyperplane to distinguish them. For each cannot-link constraint, we first compute the corresponding mid-perpendicular hyperplane and then use distances of objects to this hyperplane to learn a new data representation and similarity matrix. Finally, we combine all the similarity matrices from all cannot-link constraints into single similarity matrix and perform kernel k-means on it to obtain the partition. We implement MPHS for two cases, i.e., a simple one performed in original input space when the data set is nearly linear-separable, and an advanced one in kernel-induced feature space when the data set is complex and nonlinear-separable. Experimental results on several UCI data sets and some image data sets show the effectiveness of our method.