Balanced multi-view clustering with dynamic consistency exploration among multiple views

Graph-based multi-view clustering techniques are widely praised in many clustering tasks for their outstanding performance. The weakness of most existing graph-based multi-view clustering methods is that they tend to generate skewed clusters, which may contain few samples or even only outliers when large overlaps among clusters appear. In this case, the consistent information extracted from multiple views may be biased, potentially misguiding the multi-view clustering procedure and resulting in degraded clustering performance. Besides, conventional graph-based multi-view clustering methods usually employ spectral relaxation to optimize the graph cut problem. This solution may contain mixed signs, requiring additional clustering methods, such as K-means, to get the final results. However, this process may lead to unstable and unbalanced clustering results, even when the given samples are evenly …