Data clustering using a model granular magnet
Neural Computation
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Unifying Approach to Hard and Probabilistic Clustering
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification with partial labels
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Improving Classification with Pairwise Constraints: A Margin-Based Approach
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Boosting with pairwise constraints
Neurocomputing
Improving Hierarchical Classification with Partial Labels
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Semi-supervised discriminatively regularized classifier with pairwise constraints
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Multi-view classification with cross-view must-link and cannot-link side information
Knowledge-Based Systems
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In this paper we consider the problem of classification in the presence of pairwise constraints, which consist of pairs of examples as well as a binary variable indicating whether they belong to the same class or not. We propose a method which can effectively utilize pairwise constraints to construct an estimator of the decision boundary, and we show that the resulting estimator is sign-insensitive consistent with respect to the optimal linear decision boundary. We also study the asymptotic variance of the estimator and extend the method to handle both labeled and pairwise examples in a natural way. Several experiments on simulated datasets and real world classification datasets are conducted. The results not only verify the theoretical properties of the proposed method but also demonstrate its practical value in applications.