Cross Language Text Classification via Multi-view Subspace Learning

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.