FMvC: Fast Multi-View Clustering

In multi-view clustering, an eigen-decomposition of the Laplacian matrix of the graph is usually necessary. This leads to a significant increase in time cost and also requires post-processing such as
$k$
-means. In addition, some methods require learning a uniform graph matrix. In large-scale data, this process significantly increase time and memory costs. To address these problems, this paper proposes Fast Multi-view Clustering (FMvC). First, non-negative constraints are added to the objective function from the unified view of relaxed normalized and ratio cuts. Then, graph reconstruction is performed on the similarity matrix using an indication matrix to ensure that the obtained graph has robust intra-cluster and weak inter-cluster connectivity. Besides, the operation speed of the method can be further enhanced by setting a common labeling matrix. Finally, the problem is solved optimally based on the strategy of alternating directional multipliers. Experimental results on eight real-world datasets demonstrate the effectiveness of the proposed algorithm, which can always outperform eleven existing baseline algorithms.