Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization
<|reference_start|>Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization: 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.<|reference_end|>
@article{wang2024interpretable,
    title={Interpretable Multi-View Clustering Based on Anchor Graph Tensor
  Factorization},
    author={Rui Wang, Jing Li, Quanxue Gao, Cheng Deng},
    journal={arXiv preprint arXiv:2404.00883},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00883},
    primaryClass={cs.LG}
}