CIML combines a Gács-Körner-style common-representation objective with per-view information-bottleneck unique representations and independence constraints, reporting state-of-the-art accuracy on six multi-view datasets.
Reliable conflictive multi-view learning,
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Towards Comprehensive Information-theoretic Multi-view Learning
CIML combines a Gács-Körner-style common-representation objective with per-view information-bottleneck unique representations and independence constraints, reporting state-of-the-art accuracy on six multi-view datasets.