Auto-weighted orthogonal and nonnegative graph reconstruction for multi-view clustering

Similarity matrix is of vital importance for graph-based multi-view clustering models, which can depict the nonlinear structure information among samples. However, most existing approaches learn the common similarity graphs for clustering assignment from original data points directly, which shows the unclear structure to result in the inability to accurately extract underlying information in datasets. In this paper, a novel multi-view clustering model called AONGR is proposed, which integrates spectral clustering and nonnegative matrix factorization into a joint framework to reconstruct the similarity graphs. The reconstructed graph not only owns a clear structure but offers the interpretation for cluster assignment. The contribution of each view on clustering can also be obtained during the procedure of optimization. And an effective algorithm is designed to update the variables in AONGR. Experimental results on eight real-world datasets demonstrate the superiority of our AONGR in comparison with the state-of-the-art baselines.