Similarity metric learning for a variable-kernel classifier
Neural Computation
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering based on conditional distributions in an auxiliary space
Neural Computation
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Adaptive Kernel Metric Nearest Neighbor Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Boosting margin based distance functions for clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Locally linear metric adaptation for semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Parametric distance metric learning with label information
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Pairwise Rayleigh quotient classifier with application to the analysis of breast tumors
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Learning from pairwise constraints by Similarity Neural Networks
Neural Networks
| Hi-index | 0.00 |
Many supervised and unsupervised learning algorithms depend on the choice of an appropriate distance metric. While metric learning for supervised learning tasks has a long history, extending it to learning tasks with weaker supervisory information has only been studied very recently. In particular, several methods have been proposed for semi-supervised metric learning based on pairwise (dis)similarity information. In this paper, we propose a kernel-based approach for nonlinear metric learning, which performs locally linear translation in the kernel-induced feature space. We formulate the metric learning problem as a kernel learning problem and solve it efficiently by kernel matrix adaptation. Experimental results based on synthetic and real-world data sets show that our approach is promising for semi-supervised metric learning.