Double Self-weighted Multi-view Clustering via Adaptive View Fusion
This paper tackles the problem of noise and redundant features in multi-view clustering, which is an incremental improvement for researchers and practitioners working with multi-view data.
The paper addresses the problem of noise and redundant features in multi-view clustering by proposing Double Self-weighted Multi-view Clustering (DSMC). DSMC uses two self-weighted operations to assign different weights to features and graphs, respectively, to enhance robustness and integrate information for clustering. Experiments on six real-world datasets show its advantages over other state-of-the-art methods.
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.