Situations when only a limited amount of labeled data and a large amount of unlabeled data are available to the learning algorithm are typical for many real-world problems. To make use of unlabeled data in preference learning problems, we propose a semisupervised algorithm that is based on the multiview approach. Our algorithm, which we call Sparse Co-RankRLS, minimizes a least-squares approximation of the ranking error and is formulated within the co-regularization framework. It operates by constructing a ranker for each view and by choosing such ranking prediction functions that minimize the disagreement among all of the rankers on the unlabeled data. Our experiments, conducted on real-world dataset, show that the inclusion of unlabeled data can improve the prediction performance significantly. Moreover, our semisupervised preference learning algorithm has a linear complexity in the number of unlabeled data items, making it applicable to large datasets. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Tsivtsivadze, E., Pahikkala, T., Boberg, J., Salakoski, T., & Heskes, T. (2011). Co-regularized least-squares for label ranking. In Preference Learning (pp. 107–123). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-14125-6_6
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