.png){kind=icon}
返回
Weighted tensor-based consistent anchor graph learning for multi-view clustering
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2024.129253.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The anchor graph-based multi-view subspace clustering (MVSC) methods have shown promising performance when dealing with large-scale data. However, the consistent information is not fully explored when learning anchor graphs and exploring higher-order relationships, affecting the clustering performance. To this end, we propose a novel multi-view clustering (MVC) model called weighted tensor-based consistent anchor graph learning (WTCAGL). Specifically, we first learn the embedding anchor graphs by projecting the original data into the embedding space. Meanwhile, we learn the consistent anchor graph by concatenating features of different views, which can well capture the consistent relationship between different views to fully explore the cross-view consistent information. Furthermore, we impose the weighted low-rank tensor constraint on the embedding anchor graphs and the consistent anchor graph, which can capture the higher-order relationships between the embedding anchor graph and the consistent anchor graph. Experiments on multi-view datasets validate the effectiveness of the proposed WTCAGL method.
Keyword:
Multi-view clustering
Anchor graph
Consistent information
Weighted low-rank tensor
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Extreme Somatosensory Evoked Potential (ESEP): An EEG Sign Forecasting the Possible Occurrence of Seizures in Children.png)
Epilepsia
EpilepsiaIF0
PREAI
分享收藏
分享收藏Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance共识锚引导的无参数多视图子空间快速聚类
PREAI
分享收藏High-order manifold regularized multi-view subspace clustering with robust affinity matrices and weighted TNN具有鲁棒亲和矩阵和加权TNN的高阶流形正则化多视图子空间聚类PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏分享收藏
查看更多内容