Self-weighted Multiview Clustering with Multiple Graphs.

In multiview learning, it is essential to assign a reasonable weight to each view according to the view importance. Thus, for multiview clustering task, a wise and elegant method should achieve clustering multiview data while learning the view weights. In this paper, we propose to explore a Laplacian rank constrained graph, which can be approximately as the centroid of the built graph for each view with different confidences. We start our work with a natural thought that the weights can be learned by introducing a hyperparameter. By analyzing the weakness of this way, we further propose a new multiview clustering method which is totally self-weighted. More importantly, once the target graph is obtained in our models, we can directly assign the cluster label to each data point and do not need any postprocessing such as K -means in standard spectral clustering. Evaluations on two synthetic datasets indicate the effectiveness of our methods. Compared with several representative graphbased multiview clustering approaches on four realworld datasets, the proposed methods achieve the better performances and our new clustering method is more practical to use.