Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
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
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
A scalable kernel-based algorithm for semi-supervised metric learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Kernel-based metric learning for semi-supervised clustering
Neurocomputing
Tropical cyclone event sequence similarity search via dimensionality reduction and metric learning
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning low-rank kernel matrices for constrained clustering
Neurocomputing
Learning from pairwise constraints by Similarity Neural Networks
Neural Networks
Probabilistic non-linear distance metric learning for constrained clustering
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
Fine-grained semi-supervised labeling of large shape collections
ACM Transactions on Graphics (TOG)
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Distance metric has an important role in many machine learning algorithms. Recently, metric learning for semi-supervised algorithms has received much attention. For semi-supervised clustering, usually a set of pairwise similarity and dissimilarity constraints is provided as supervisory information. Until now, various metric learning methods utilizing pairwise constraints have been proposed. The existing methods that can consider both positive (must-link) and negative (cannot-link) constraints find linear transformations or equivalently global Mahalanobis metrics. Additionally, they find metrics only according to the data points appearing in constraints (without considering other data points). In this paper, we consider the topological structure of data along with both positive and negative constraints. We propose a kernel-based metric learning method that provides a non-linear transformation. Experimental results on synthetic and real-world data sets show the effectiveness of our metric learning method.