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Co-Regularized Least-Squares for Label Ranking

Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski, Tom Heskes, Co-Regularized Least-Squares for Label Ranking. In: Johannes Fürnkranz, Eyke Hüllermeier (Eds.), Preference Learning, 107–123, Springer, 2010.

Abstract:

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.

BibTeX entry:

@INBOOK{cTsPaBoSaHe10a,
  title = {Co-Regularized Least-Squares for Label Ranking},
  booktitle = {Preference Learning},
  author = {Tsivtsivadze, Evgeni and Pahikkala, Tapio and Boberg, Jorma and Salakoski, Tapio and Heskes, Tom},
  editor = {Fürnkranz, Johannes and Hüllermeier, Eyke},
  publisher = {Springer},
  pages = {107–123},
  year = {2010},
}

Belongs to TUCS Research Unit(s): Turku BioNLP Group

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