Information-theoretic multi-view domain adaptation

Authors:
Pei Yang;Wei Gao;Qi Tan;Kam-Fai Wong
Affiliations:
South China University of Technology, Guangzhou, China and The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;Qatar Computing Research Institute, Qatar Foundation, Doha, Qatar;South China University of Technology, Guangzhou, China;The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
Venue:
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Year:
2012

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Abstract

We use multiple views for cross-domain document classification. The main idea is to strengthen the views' consistency for target data with source training data by identifying the correlations of domain-specific features from different domains. We present an Information-theoretic Multi-view Adaptation Model (IMAM) based on a multi-way clustering scheme, where word and link clusters can draw together seemingly unrelated domain-specific features from both sides and iteratively boost the consistency between document clusterings based on word and link views. Experiments show that IMAM significantly outperforms state-of-the-art baselines.