Multi-view spectral clustering based on constrained Laplacian rank

Abstract

The graph-based approach is a representative clustering method among multi-view clustering algorithms. However, it remains a challenge to quickly acquire complementary information in multi-view data and to execute effective clustering when the quality of the initially constructed data graph is inadequate. Therefore, we propose multi-view spectral clustering based on constrained Laplacian rank method, a new graph-based method (CLRSC). The following are our contributions: (1) Self-representation learning and CLR are extended to multi-view and they are connected into a unified framework to learn a common affinity matrix. (2) To achieve the overall optimization we construct a graph learning method based on constrained Laplacian rank and combine it with spectral clustering. (3) An iterative optimization-based procedure we designed and showed that our algorithm is convergent. Finally, sufficient experiments are carried out on 5 benchmark datasets. The experimental results on MSRC-v1 and BBCSport datasets show that the accuracy (ACC) of the method is 10.95% and 4.61% higher than the optimal comparison algorithm, respectively.