Multi-view clustering with constructed bipartite graph in embedding space
作者：
Highlights：
• The proposed multi-view clustering method jointly learns features of objects and structure of clusters in embedding space.
• We transform the multi-view clustering into a constrained optimization problem with Laplacian rank.
• Experimental results demonstrate that the proposed algorithm achieves the best performance with the least running time.
摘要
•The proposed multi-view clustering method jointly learns features of objects and structure of clusters in embedding space.•We transform the multi-view clustering into a constrained optimization problem with Laplacian rank.•Experimental results demonstrate that the proposed algorithm achieves the best performance with the least running time.
论文关键词：Multi-view clustering,Graph embedding,Joint learning,Nonnegative matrix factorization
论文评审过程：Received 1 October 2020, Revised 8 June 2022, Accepted 11 August 2022, Available online 17 August 2022, Version of Record 27 August 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2022.109690
