Low-rank tensor approximation with local structure for multi-view intrinsic subspace clustering

DOI10.1016/J.INS.2022.05.091OpenAlexW4281660838WikidataQ114167338 ScholiaQ114167338MaRDI QIDQ6191174

Lele Fu, Jing Hua Yang, Chuan Chen, ChuanFu Zhang

Publication date: 6 March 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2022.05.091

zbMATH Keywords

low-rank tensormulti-view clusteringsubspace representationlocal manifold

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Cites Work

• On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
• Auto-weighted multi-view co-clustering with bipartite graphs
• Learning robust affinity graph representation for multi-view clustering
• Tensorized multi-view subspace representation learning
• Learning a consensus affinity matrix for multi-view clustering via subspaces merging on Grassmann manifold
• Multiple kernel low-rank representation-based robust multi-view subspace clustering
• On unifying multi-view self-representations for clustering by tensor multi-rank minimization
• Procrustes Problems
• Multi-View Learning With Incomplete Views
• Essential Tensor Learning for Multi-View Spectral Clustering
• Algorithm 432 [C2: Solution of the matrix equation AX + XB = C [F4]]
• Fine-grained similarity fusion for multi-view spectral clustering

This page was built for publication: Low-rank tensor approximation with local structure for multi-view intrinsic subspace clustering