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Deep incomplete multi-view representation learning with doubly relation transfer
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AI
DOI：10.1016/j.asoc.2026.114735.png)
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Abstract
En 中文
• Introducing a simple yet effective doubly cross-view sample relation transfer strategy for the incomplete multi-view problem. • Conducting missing information imputation at two levels: the original data (model input) and the representations. • Handling both partially and fully incomplete scenarios effectively. • Experiments on various datasets with various missing rates verify the model effectiveness.
Keywords:
multi-view learning
missing data imputation
representation learning
relation transfer
incomplete data
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