On the convergence of the coordinate descent method for convex differentiable minimization
Journal of Optimization Theory and Applications
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Solving multiclass support vector machines with LaRank
Proceedings of the 24th international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Kernel regression with order preferences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Efficient multi-class unlabeled constrained semi-supervised SVM
Proceedings of the 18th ACM conference on Information and knowledge management
Probabilistic Labeled Semi-supervised SVM
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Semi-Supervised Learning
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Problems of ordinal regression arise in many fields such as information retrieval, data mining and knowledge management. In this paper, we consider ordinal regression in a semi-supervised scenario, i.e., we try to utilize the ordinal information from the distribution of unlabeled data. Semi-supervised ordinal regression is more applicable than traditional supervised ordinal regression, because nowadays labeled data is expensive and time-consuming as it needs human labor, whereas a large amount of unlabeled data are far accessible with the development of internet technology. We construct a general semi-supervised ordinal regression framework to formulate this problem. Based on the framework, we then propose a semi-supervised ordinal regression method called Semi-supervised Ordinal SVM (SOSVM). Additionally, in order to make our proposed method more applicable to problems with large scaled labeled data, we put forward a kernel based dual coordinate descent algorithm to efficiently solve SOSVM. Both rigorous theoretical analysis and promising experimental evaluations on real world datasets show the great performance and remarkable efficiency of SOSVM.