  Multi-view clustering has been applied in many real-world applications where
original data often contain noises. Some graph-based multi-view clustering
methods have been proposed to try to reduce the negative influence of noises.
However, previous graph-based multi-view clustering methods treat all features
equally even if there are redundant features or noises, which is obviously
unreasonable. In this paper, we propose a novel multi-view clustering framework
Double Self-weighted Multi-view Clustering (DSMC) to overcome the
aforementioned deficiency. DSMC performs double self-weighted operations to
remove redundant features and noises from each graph, thereby obtaining robust
graphs. For the first self-weighted operation, it assigns different weights to
different features by introducing an adaptive weight matrix, which can
reinforce the role of the important features in the joint representation and
make each graph robust. For the second self-weighting operation, it weights
different graphs by imposing an adaptive weight factor, which can assign larger
weights to more robust graphs. Furthermore, by designing an adaptive multiple
graphs fusion, we can fuse the features in the different graphs to integrate
these graphs for clustering. Experiments on six real-world datasets demonstrate
its advantages over other state-of-the-art multi-view clustering methods.
