Tensorized Bipartite Graph Learning for Multi-View Clustering

Despite the impressive clustering performance and efficiency in characterizing both the relationship between the data and cluster structure, most existing graph-based multi-view clustering methods still have the following drawbacks. They suffer from the expensive time burden due to both the construction of graphs and eigen-decomposition of Laplacian matrix. Moreover, none of them simultaneously considers the similarity of inter-view and similarity of intra-view. In this article, we propose a variance-based de-correlation anchor selection strategy for bipartite construction. The selected anchors not only cover the whole classes but also characterize the intrinsic structure of data. Following that, we present a…

• NN
  National Natural Science Foundation of China

  Awards: 62176203, 62036007
• NL
  National Laboratory of Pattern Recognition

  Award: 202200035
• NS
  Natural Science Basic Research Program of Shaanxi Province

  Award: 2020JZ-19