Multi-View Clustering via Graph Regularized Symmetric Nonnegative Matrix Factorization

Multi-view clustering has become a hot topic since the past decade and nonnegative matrix factorization (NMF) based multi-view clustering algorithms have shown their superiorities. Nevertheless, two drawbacks prevent NMF based multi-view algorithms from being a better algorithm: (1) The solution of NMF based multi-view algorithms is not unique. (2) Standard orthogonal basis matrix is not obtained for each view. Orthogonality is utilized to settle these above problems in our framework and high computational complexity caused by orthogonality is avoided. Moreover, to preserve the locally geometrical structure between views, graph regularization is utilized. Finally, we offer an update rule for the parameter of the graph regularization to balance the reconstruct error and regularization and make the objective function converge faster. Experimental results and theoretical proof show the validity and efficiency of our algorithm.