Information-theoretic multi-view domain adaptation

• Main Authors: YANG, Pei, GAO, Wei, TAN, Qi, WONG, Kam-Fai
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/4590; https://ink.library.smu.edu.sg/context/sis_research/article/5593/viewcontent/P12_2053.pdf
• Institution: Singapore Management University

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.