Flexible and Parameter-Free Graph Learning for Multi-View Spectral Clustering

With the extensive use of multi-view data in practice, multi-view spectral clustering has received a lot of attention. In this work, we focus on the following two challenges, namely, how to deal with the partially contradictory graph information among different views and how to conduct clustering without the parameter selection. To this end, we establish a novel graph learning framework, which avoids the linear combination of the partially contradictory graph information among different views and learns a unified graph for clustering without the parameter selection. Specifically, we introduce a flexible graph degeneration with a structured graph constraint to address the aforementioned challenging issues. Besides, our method can be employed to deal with large-scale data by using the bipartite graph. Experimental results show the effectiveness and competitiveness of our method, compared to several state-of-the-art methods.