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Virtual Regularized Bipartite Graph Learning for Multi-View Subspace Clustering
DOI:10.1016/j.knosys.2025.114392.png)
摘要
En 中文
• 使用锚点和二分图处理大规模数据集。
• 通过投影生成判别性锚图。
• 新的虚拟正则化(VR)以指导二分图生成。
• 三个部分在统一框架中相互强化。
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Multi-view clustering via pseudo-label guide learning and latent graph structure recovery
PATTERN RECOGNITION
IF7.6
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
Adaptive multi-view subspace clustering algorithm based on representative features and redundant instances
NEUROCOMPUTING
IF6.5
Deep Contrastive Multi-View Subspace Clustering With Representation and Cluster Interactive Learning具有表示和簇交互学习的深度对比多视图子空间聚类
Similarity network fusion for aggregating data types on a genomic scale用于在基因组规模上聚合数据类型的相似性网络融合
NATURE METHODS
IF32.1
Anchor-based multi-view subspace clustering with hierarchical feature descent基于Anchor的分层特征下降的多视图子空间聚类
INFORMATION FUSION
IF15.5

