Towards a unified framework for graph-based multi-view clustering

Recently, clustering data collected from various sources has become a hot topic in real-world applications. The most common methods for multi-view clustering can be divided into several categories: Spectral clustering algorithms, subspace multi-view clustering algorithms, matrix factorization approaches, and kernel methods. Despite the high performance of these methods, they directly fuse all similarity matrices of all views and separate the affinity learning process from the multiview clustering process. The performance of these algorithms can be affected by noisy affinity matrices. To overcome this drawback, this paper presents a novel method called One Step Multi-view Clustering via Consensus Graph Learning and Nonnegative Embedding (OSMGNE). Instead of directly merging the similarity matrices of different views, which may contain noise, a step of learning a consensus similarity matrix is performed. This step forces the similarity matrices of different views to be too similar, which eliminates the problem of noisy data. Moreover, the use of the nonnegative embedding matrix (soft cluster assignment matrix makes it possible to directly obtain the final clustering result without any extra step. The proposed method can solve five subtasks simultaneously. It jointly estimates the similarity matrix of all views, the similarity matrix of each view, the corresponding spectral projection matrix, the unified clustering indicator matrix, and automatically gives the weight of each view without the use of hyper-parameters. In addition, another version of our method is also studied in this paper. This method differs from the first one by using a consensus spectral projection matrix and a consensus Laplacian matrix over all views. An iterative algorithm is proposed to solve the optimization problem of these two methods. The two proposed methods are tested on several real datasets, which prove their superiority.