Information-theoretic multi-view domain adaptation
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 Adaptatio...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2012
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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