  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}.
