LAUSR

Most existing multi-view graph clustering models either seek consistent clustering results from similarity matrices and spectral embeddings respectively or follow direct bidirectional integration of them, which ignores the interaction between them. To make up for this flaw, this paper designs a novel multi-view clustering model that performs Bidirectional Fusion with Cross-view Graph Filter (BF-CGF). To be specific, BF-CGF first learns a consistent graph embedding via performing the interaction between multi-view graphs and spectral embeddings with the perspective of the graph spectral domain and then considers seeking a consistent indicator matrix via the graph cut model from the consistent graph embedding and the similarity matrices. To solve the optimization problem of BF-CGF, we propose an efficient iterative algorithm and provide the corresponding convergence and complexity analyses. Extensive experimental results demonstrate that the proposed BF-CGF outperforms state-of-the-art competitors in most benchmark datasets.