CPM-Nets: Cross Partial Multi-View Networks
Changqing Zhang · Zongbo Han · yajie cui · Huazhu Fu · Joey Tianyi Zhou · Qinghua Hu

Thu Dec 12th 05:00 -- 07:00 PM @ East Exhibition Hall B + C #29

Despite multi-view learning progressed fast in past decades, it is still challenging due to the difficulty in modeling complex correlation among different views, especially under the context of view missing. To address the challenge, we propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets). In this framework, we first give a formal definition of completeness and versatility for multi-view representation and then theoretically prove the versatility of the latent representation learned from our algorithm. To achieve the completeness, the task of learning latent multi-view representation is specifically translated to degradation process through mimicking data transmitting, such that the optimal tradeoff between consistence and complementarity across different views could be achieved. In contrast with methods that either complete missing views or group samples according to view-missing patterns, our model fully exploits all samples and all views to produce structured representation for interpretability. Extensive experimental results validate the effectiveness of our algorithm over existing state-of-the-arts.

Author Information

Changqing Zhang (Tianjin University)
Zongbo Han (Tianjin University)
yajie cui (tianjin university)
Huazhu Fu (Inception Institute of Artificial Intelligence)
Joey Tianyi Zhou (IHPC, A*STAR)
Qinghua Hu (Tianjin University)

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