一致性引导的自适应加权多视图聚类

随着获取多模态或多视图数据的日益容易，多视图聚类研究受到广泛关注.然而，很多方法直接从原始数据中学习邻接矩阵，忽视了数据中噪声的影响.此外，还有一些方法将各个视图同等对待，而实际上各视图在聚类过程中所发挥的作用是不同的.为解决上述问题，提出了一种基于Markov链的聚类算法，名为一致性引导的自适应加权多视图聚类(consensus guided auto-weighted multi-view clustering, CAMC).首先为每个视图构造转移概率矩阵；然后，以自适应加权的方式获得一致性转移概率矩阵，并对一致性转移概率矩阵的拉普拉斯矩阵进行了秩约束，确保拉普拉斯图中连通分量的数目正好等于簇的数目.此外，基于交替方向乘子法(alternating direction method of multipliers, ADMM)优化策略对问题进行求解.在1个人造数据集和7个真实数据集上的实验结果证明了该算法的有效性，其聚类性能优于现有的8种基准算法.

Consensus Guided Auto-Weighted Multi-View Clustering

As it becomes increasingly easier to obtain multi-modal or multi-view data, multi-view clustering has gained much more attention recently. However, many methods learn the affinity matrix from the original data and may lead to unsatisfying results because of the noise in the raw dataset. Besides, some methods neglect the diversity of roles played by different views and take them equally. In this paper, we propose a novel Markov chain algorithm named consensus guided auto-weighted multi-view clustering (CAMC) to tackle these problems. A transition probability matrix is constructed for each view to learn the affinity matrix indirectly to reduce the effects of redundancies and noise in the original data. The consensus transition probability matrix is obtained in an auto-weighted way, in which the optimal weight for each view is gained automatically. Besides, a constrained Laplacian rank is utilized on the consensus transition probability to ensure that the number of the connected components in the Laplacian graph is exactly equal to that of the clusters. Moreover, an optimization strategy based on alternating direction method of multiplier (ADMM) is proposed to solve the problem. The effectiveness of the proposed algorithm is verified on a toy dataset. Extensive experiments on seven real-world datasets with different types show that CAMC outperforms the other eight benchmark algorithms in terms of clustering.