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
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Convex Optimization
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
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
Semi-supervised learning by mixed label propagation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Semi-Supervised Learning
Learning instance specific distances using metric propagation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Clustering with Constrained Similarity Learning
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
A graph-based projection approach for semi-supervised clustering
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Performance evaluation of constraints in graph-based semi-supervised clustering
AMT'10 Proceedings of the 6th international conference on Active media technology
Constrained spectral clustering via exhaustive and efficient constraint propagation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Semisupervised kernel matrix learning by kernel propagation
IEEE Transactions on Neural Networks
Learning low-rank kernel matrices for constrained clustering
Neurocomputing
Pairwise constraint propagation for graph-based semi-supervised clustering
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Graph-Cut Based Iterative Constrained Clustering
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Multi-modal constraint propagation for heterogeneous image clustering
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Manifold-Regularized minimax probability machine
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Semi-supervised learning with mixed knowledge information
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast semi-supervised clustering with enhanced spectral embedding
Pattern Recognition
Integrating Spectral Kernel Learning and Constraints in Semi-Supervised Classification
Neural Processing Letters
Influence of erroneous pairwise constraints in semi-supervised clustering
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Semi-supervised learning with nuclear norm regularization
Pattern Recognition
Efficient kernel learning from side information using ADMM
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Multi-view classification with cross-view must-link and cannot-link side information
Knowledge-Based Systems
Automatic image segmentation using constraint learning and propagation
Digital Signal Processing
Laplacian minimax probability machine
Pattern Recognition Letters
Rectifying the representation learned by Non-negative Matrix Factorization
International Journal of Knowledge-based and Intelligent Engineering Systems
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We consider the general problem of learning from both pairwise constraints and unlabeled data. The pairwise constraints specify whether two objects belong to the same class or not, known as the must-link constraints and the cannot-link constraints. We propose to learn a mapping that is smooth over the data graph and maps the data onto a unit hypersphere, where two must-link objects are mapped to the same point while two cannot-link objects are mapped to be orthogonal. We show that such a mapping can be achieved by formulating a semidefinite programming problem, which is convex and can be solved globally. Our approach can effectively propagate pairwise constraints to the whole data set. It can be directly applied to multi-class classification and can handle data labels, pairwise constraints, or a mixture of them in a unified framework. Promising experimental results are presented for classification tasks on a variety of synthetic and real data sets.