Cross Language Text Classification via Multi-view Subspace Learning

author: Yuhong Guo, Department of Computer and Information Sciences, Temple University
published: Jan. 11, 2013,   recorded: December 2012,   views: 4437
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Description

Cross language classification is an important task in multilingual learning, aiming for reducing the labeling cost of training a different classification model for each individual language. In this paper we develop a novel subspace co-regularized multi-view learning method for cross language text classification. The empirical study on a set of cross language text classification tasks shows the proposed method consistently outperforms a number of inductive methods, domain adaptation methods, and multi-view learning methods.

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Download slides icon Download slides: nipsworkshops2012_guo_subspace_learning_01.pdf (463.7┬áKB)


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