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
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning a Similarity Metric Discriminatively, with Application to Face Verification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
On the value of pairwise constraints in classification and consistency
Proceedings of the 24th international conference on Machine learning
Improving Hierarchical Classification with Partial Labels
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Image annotation with weak labels
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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In this paper, we address the problem of learning when some cases are fully labeled while other cases are only partially labeled, in the form of partial labels. Partial labels are represented as a set of possible labels for each training example, one of which is the correct label. We introduce a discriminative learning approach that incorporates partial label information into the conventional margin-based learning framework. The partial label learning problem is formulated as a convex quadratic optimization minimizing the L2-norm regularized empirical risk using hinge loss. We also present an efficient algorithm for classification in the presence of partial labels. Experiments with different data sets show that partial label information improves the performance of classification when there is traditional fully-labeled data, and also yields reasonable performance in the absence of any fully labeled data.