Multi-view representation learning is essential for many multi-view tasks, such as clustering and classification. However, there are two challenging problems plaguing the community: i)how to learn robust multi-view representation from mass unlabeled data and ii) how to balance the view consistency and the view specificity. To this end, in this paper, we proposed a hybrid contrastive fusion algorithm to extract robust view-common representation from unlabeled data. Specifically, we found that introducing an additional representation space and aligning representations on this space enables the model to learn robust view-common representations. At the same time, we designed an asymmetric contrastive strategy to ensure that the model does not obtain trivial solutions. Experimental results demonstrated that the proposed method outperforms 12 competitive multi-view methods on four real-world datasets in terms of clustering and classification. Our source code will be available soon at \url{https://github.com/guanzhou-ke/mori-ran}.
翻译:多视角代表学习对于许多多视角任务(如集群和分类)至关重要。然而,在社区中存在两个困扰社区的棘手问题:一)如何从质量无标签数据中学习强健的多视角代表,二)如何平衡观点的一致性和观点的特殊性。为此,我们在本文件中建议采用一种混合对比混合算法,从无标签数据中获取强健的视图-共性代表。具体地说,我们发现,引入一个额外的代表空间和对称使该空间的演示能够让模型学习稳健的视角共同表述。与此同时,我们设计了一个不对称的对比战略,以确保模型不会获得微不足道的解决方案。实验结果表明,拟议方法在组合和分类方面优于四个真实世界数据集的12种竞争性多视角方法。我们的源代码将很快在\url{https://github.com/guanzou-ke/mori-ran}上公布。