Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
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
Boosting margin based distance functions for clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning with Constrained and Unlabelled Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Mixture Modeling with Pairwise, Instance-Level Class Constraints
Neural Computation
Semi-supervised clustering: probabilistic models, algorithms and experiments
Semi-supervised clustering: probabilistic models, algorithms and experiments
Learning Distance Metrics with Contextual Constraints for Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Clustering, dimensionality reduction, and side information
Clustering, dimensionality reduction, and side information
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
Semisupervised Clustering with Metric Learning using Relative Comparisons
IEEE Transactions on Knowledge and Data Engineering
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
SimpleNPKL: simple non-parametric kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Parametric distance metric learning with label information
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A Kernel Approach for Semisupervised Metric Learning
IEEE Transactions on Neural Networks
Learning from pairwise constraints by Similarity Neural Networks
Neural Networks
Fast neighborhood component analysis
Neurocomputing
Semi-supervised clustering with discriminative random fields
Pattern Recognition
Active selection of clustering constraints: a sequential approach
Pattern Recognition
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The problem of clustering with side information has received much recent attention and metric learning has been considered as a powerful approach to this problem. Until now, various metric learning methods have been proposed for semi-supervised clustering. Although some of the existing methods can use both positive (must-link) and negative (cannot-link) constraints, they are usually limited to learning a linear transformation (i.e., finding a global Mahalanobis metric). In this paper, we propose a framework for learning linear and non-linear transformations efficiently. We use both positive and negative constraints and also the intrinsic topological structure of data. We formulate our metric learning method as an appropriate optimization problem and find the global optimum of this problem. The proposed non-linear method can be considered as an efficient kernel learning method that yields an explicit non-linear transformation and thus shows out-of-sample generalization ability. Experimental results on synthetic and real-world data sets show the effectiveness of our metric learning method for semi-supervised clustering tasks.