Similarity metric learning for a variable-kernel classifier
Neural Computation
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Semi-supervised clustering: probabilistic models, algorithms and experiments
Semi-supervised clustering: probabilistic models, algorithms and experiments
Learning low-rank kernel matrices
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
A transductive framework of distance metric learning by spectral dimensionality reduction
Proceedings of the 24th international conference on Machine learning
Metric learning with convex optimization
Metric learning with convex optimization
Towards effective document clustering: A constrained K-means based approach
Information Processing and Management: an International Journal
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Semi-supervised graph clustering: a kernel approach
Machine Learning
SimpleNPKL: simple non-parametric kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Low-Rank Kernel Learning with Bregman Matrix Divergences
The Journal of Machine Learning Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IEEE Transactions on Neural Networks
Semi-supervised metric learning using pairwise constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Semi-supervised clustering with metric learning: An adaptive kernel method
Pattern Recognition
Kernel-based metric learning for semi-supervised clustering
Neurocomputing
An Optimal Global Nearest Neighbor Metric
IEEE Transactions on Pattern Analysis and Machine Intelligence
The optimal distance measure for nearest neighbor classification
IEEE Transactions on Information Theory
Spectral K-way ratio-cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Large margin nearest neighbor classifiers
IEEE Transactions on Neural Networks
A Kernel Approach for Semisupervised Metric Learning
IEEE Transactions on Neural Networks
A study of K-Means-based algorithms for constrained clustering
Intelligent Data Analysis
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Constrained clustering methods (that usually use must-link and/or cannot-link constraints) have been received much attention in the last decade. Recently, kernel adaptation or kernel learning has been considered as a powerful approach for constrained clustering. However, these methods usually either allow only special forms of kernels or learn non-parametric kernel matrices and scale very poorly. Therefore, they either learn a metric that has low flexibility or are applicable only on small data sets due to their high computational complexity. In this paper, we propose a more efficient non-linear metric learning method that learns a low-rank kernel matrix from must-link and cannot-link constraints and the topological structure of data. We formulate the proposed method as a trace ratio optimization problem and learn appropriate distance metrics through finding optimal low-rank kernel matrices. We solve the proposed optimization problem much more efficiently than SDP solvers. Additionally, we show that the spectral clustering methods can be considered as a special form of low-rank kernel learning methods. Extensive experiments have demonstrated the superiority of the proposed method compared to recently introduced kernel learning methods.