keyword
multi-view subspace clustering
Multi-view subspace clustering is an unsupervised machine learning technique that groups data objects described by multiple distinct feature representations or modalities under the assumption that the data points lie within a union of low-dimensional subspaces. It extends traditional single-view subspace clustering by simultaneously uncovering the latent subspace structures across different views and integrating their complementary and shared characteristics into a unified representation. By constructing a joint similarity graph or consensus subspace from diverse feature sets, this approach effectively captures the underlying geometric structure of complex, high-dimensional data to produce consistent and accurate cluster assignments across all views.
2 items
Hard Sample Aware Network for Contrastive Deep Graph Clustering
Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Zhen Wang, Ke Liang, Wenxuan Tu, Liang Li, Jingcan Duan, Cancan Chen
Why you should read this
Proposes a contrastive deep graph clustering framework that integrates attribute and topological similarities to dynamically identify and up-weight both hard positive and hard negative node pairs using high-confidence pseudo-labels.
Added
2026-10-05
Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors
Suyuan Liu, Siwei Wang, Pei Zhang, Kai Xu, Xinwang Liu, Changwang Zhang, Feng Gao
Why you should read this
Proposes a scalable multi-view subspace clustering method that jointly learns consensus anchors and a fused graph with exact connected components, achieving linear time complexity and directly generating cluster labels without heuristic anchor sampling or post-processing steps.
Multi-view subspace clustering (MVSC) optimally integrates multiple graph structure information to improve clustering performance. Recently, many anchor-based variants are proposed to reduce the computational complexity of MVSC. Though achieving considerable acceleration, we observe that most of them adopt fixed anchor points separating from the sub-sequential anchor graph construction, which may adversely affect the clustering performance. In addition, post-processing is required to generate discrete clustering labels with additional time consumption. To address these issues, we propose a scalable and parameter-free MVSC method to directly output the clustering labels with optimal anchor graph, termed as Efficient One-pass Multi-view Subspace Clustering with Consensus Anchors (EOMSC-CA). Specially, we combine anchor learning and graph construction into a uniform framework to boost clustering performance. Meanwhile, by imposing a graph connectivity constraint, our algorithm directly outputs the clustering labels without any post-processing procedures as previous methods do. Our proposed EOMSC-CA is proven to be linear complexity respecting to the data size. The superiority of our EOMSC-CA over the effectiveness and efficiency is demonstrated by extensive experiments. Our code is publicly available at https://github.com/Tracesource/EOMSC-CA.
Added
2026-09-26