Semi-supervised multi-view learning has been an important research topic due to its capability to exploit complementary information from unlabeled multi-view data. This work proposes MMatch, a new semi-supervised discriminative representation learning method for multi-view classification. Unlike existing multi-view representation learning methods that seldom consider the negative impact caused by particular views with unclear classification structures (weak discriminative views). MMatch jointly learns view-specific representations and class probabilities of training data. The representations concatenated to integrate multiple views’ information to form a global representation. Moreover, MMatch performs the smoothness constraint on the class probabilities of the global representation to improve pseudo labels, whereas the pseudo labels regularize the structure of view-specific representations. A discriminative global representation is mined with the training process, and the negative impact of weak discriminative views is overcome. Besides, MMatch learns consistent classification while preserving diverse information from multiple views. Experiments on several multi-view datasets demonstrate the effectiveness of MMatch.

中文翻译：

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MMatch：多视图分类的半监督判别表示学习


半监督多视图学习由于其能够从未标记的多视图数据中利用互补信息而成为一个重要的研究课题。这项工作提出了 MMatch，一种新的用于多视图分类的半监督判别表示学习方法。与现有的多视图表示学习方法不同，现有的多视图表示学习方法很少考虑分类结构不明确的特定视图（弱判别性视图）所造成的负面影响。 MMatch 联合学习训练数据的视图特定表示和类概率。这些表示连接起来整合多个视图的信息以形成全局表示。此外，MMatch 对全局表示的类概率执行平滑约束以改进伪标签，而伪标签则规范特定于视图的表示的结构。通过训练过程挖掘具有歧视性的全局代表性，并克服弱歧视性观点的负面影响。此外，MMatch 可以学习一致的分类，同时保留来自多个视图的不同信息。在多个多视图数据集上的实验证明了 MMatch 的有效性。