Multi-view learning receives increasing interest in recent years to analyze complex data. Lately, multi-view maximum entropy discrimination (MVMED) and alternative MVMED (AMVMED) were proposed as extensions of maximum entropy discrimination (MED) to the multi-view learning setting, which use the hard margin consistency principle that enforces two view margins to be the same. In this paper, we propose soft margin consistency based multi-view MED (SMVMED) achieving margin consistency in a less strict way, which minimizes the relative entropy between the posteriors of two view margins. With a trade-off parameter balancing large margin and margin consistency, SMVMED is more flexible. We also propose a sequential minimal optimization (SMO) algorithm to efficiently train SMVMED and make it scalable to large datasets. We evaluate the performance of SMVMED on multiple real-world datasets and get encouraging results.
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