Multiview self-learning

Abstract : In many applications, observations are available with different views. This is, for example, the case with image-text classification, multilingual document classification or document classification on the web. In addition, unlabeled multiview examples can be easily acquired, but assigning labels to these examples is usually a time consuming task. We describe a multiview self-learning strategy which trains different voting classifiers on different views. The margin distributions over the unlabeled training data, obtained with each view-specific classifier are then used to estimate an upper-bound on their transductive Bayes error. Minimizing this upper-bound provides an automatic margin-threshold which is used to assign pseudo-labels to unlabeled examples. Final class labels are then assigned to these examples, by taking a vote on the pool of the previous pseudo-labels. New view-specific classifiers are then trained using the labeled and pseudo-labeled training data. We consider applications to image-text classification and to multilingual document classification. We present experimental results on the NUS-WIDE collection and on Reuters RCV1-RCV2 which show that despite its simplicity, our approach is competitive with other state-of-the-art techniques.
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Contributeur : Massih-Reza Amini <>
Soumis le : dimanche 4 octobre 2015 - 07:54:17
Dernière modification le : jeudi 11 octobre 2018 - 08:48:04



Ali Fakeri-Tabrizi, Massih-Reza Amini, Goutte Cyril, Nicolas Usunier. Multiview self-learning. Neurocomputing, Elsevier, 2015, 155, pp.117-127. 〈〉. 〈10.1016/j.neucom.2014.12.041〉. 〈hal-01211216〉



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