Interpretable Multi-View Clustering Based on Anchor Graph Tensor
  Factorization
Jing Li Quanxue Gao Cheng Deng Qianqian Wang Ming Yang
The clustering method based on the anchor graph has gained significant
attention due to its exceptional clustering performance and ability to process
large-scale data. One common approach is to learn bipartite graphs with
K-connected components, helping avoid the need for post-processing. However,
this method has strict parameter requirements and may not always get
K-connected components. To address this issue, an alternative approach is to
directly obtain the cluster label matrix by performing non-negative matrix
factorization (NMF) on the anchor graph. Nevertheless, existing multi-view
clustering methods based on anchor graph factorization lack adequate cluster
interpretability for the decomposed matrix and often overlook the inter-view
information. We address this limitation by using non-negative tensor
factorization to decompose an anchor graph tensor that combines anchor graphs
from multiple views. This approach allows us to consider inter-view information
comprehensively. The decomposed tensors, namely the sample indicator tensor and
the anchor indicator tensor, enhance the interpretability of the factorization.
Extensive experiments validate the effectiveness of this method.