Embedded Anchors Coupled Low-Rank Tensor Learning for Multi-View Intrinsic Subspace Clustering
Multi-view subspace clustering mines fusion maps that reflect the underlying structure of views in low-dimensional subspace. It has been broadly popularized for its capability to consolidate multi-view information effectively. The cubic time complexity of both graph construction and spectral cluster...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11020673/ |
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| author | Yueyao Li Yanying Mei Zhenwen Ren Bin Wu |
| author_facet | Yueyao Li Yanying Mei Zhenwen Ren Bin Wu |
| author_sort | Yueyao Li |
| collection | DOAJ |
| description | Multi-view subspace clustering mines fusion maps that reflect the underlying structure of views in low-dimensional subspace. It has been broadly popularized for its capability to consolidate multi-view information effectively. The cubic time complexity of both graph construction and spectral clustering has greatly hindered the application of these algorithms to large-scale scenarios. Several research approaches have dramatically improved the clustering productivity by employing the anchor sampling mechanism. However, existing methods do not consider that large-scale data often includes extensive noise and anomalous information, which inevitably degrades the clustering performance by performing the selection of anchors in the primitive data space. Besides, these methods do not reveal the high-order relationships concealed behind multi-view data and recover the global low-rank of the anchor graphs. Given this, we present a new approach called embedded anchors coupled low-rank tensor learning for multi-view intrinsic subspace clustering (ALTMSC). Specifically, we firstly map the multi-view data to a clean feature space through the feature transfer matrix, and adaptively accomplish anchor learning and the construction of embedded anchor graphs. As such, we can obtain high-quality anchor graphs. In addition, to enhance cross-view global consistency, we learn multiple intrinsic anchor graphs via rank-preserving decomposition to diminish the negative impact of view-specific statistical properties on consistency. Then, these intrinsic anchor maps are stacked into a third-order tensor with tensor nuclear norm constraint that can fully explore the high-order relationships between views. Compared with existing approaches, extensive experiments on eight datasets confirm the supremacy of ALTMSC. |
| format | Article |
| id | doaj-art-97b8711f953045eea2d3dd662c8b8845 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-97b8711f953045eea2d3dd662c8b88452025-08-20T03:31:23ZengIEEEIEEE Access2169-35362025-01-011310131310132710.1109/ACCESS.2025.357551711020673Embedded Anchors Coupled Low-Rank Tensor Learning for Multi-View Intrinsic Subspace ClusteringYueyao Li0https://orcid.org/0009-0008-3679-6609Yanying Mei1https://orcid.org/0000-0001-8886-4396Zhenwen Ren2https://orcid.org/0000-0003-3791-9750Bin Wu3https://orcid.org/0000-0002-6017-8332School of Information Engineering, Southwest University of Science and Technology, Mianyang, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang, ChinaSchool of National Defense Science and Technology, Southwest University of Science and Technology, Mianyang, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang, ChinaMulti-view subspace clustering mines fusion maps that reflect the underlying structure of views in low-dimensional subspace. It has been broadly popularized for its capability to consolidate multi-view information effectively. The cubic time complexity of both graph construction and spectral clustering has greatly hindered the application of these algorithms to large-scale scenarios. Several research approaches have dramatically improved the clustering productivity by employing the anchor sampling mechanism. However, existing methods do not consider that large-scale data often includes extensive noise and anomalous information, which inevitably degrades the clustering performance by performing the selection of anchors in the primitive data space. Besides, these methods do not reveal the high-order relationships concealed behind multi-view data and recover the global low-rank of the anchor graphs. Given this, we present a new approach called embedded anchors coupled low-rank tensor learning for multi-view intrinsic subspace clustering (ALTMSC). Specifically, we firstly map the multi-view data to a clean feature space through the feature transfer matrix, and adaptively accomplish anchor learning and the construction of embedded anchor graphs. As such, we can obtain high-quality anchor graphs. In addition, to enhance cross-view global consistency, we learn multiple intrinsic anchor graphs via rank-preserving decomposition to diminish the negative impact of view-specific statistical properties on consistency. Then, these intrinsic anchor maps are stacked into a third-order tensor with tensor nuclear norm constraint that can fully explore the high-order relationships between views. Compared with existing approaches, extensive experiments on eight datasets confirm the supremacy of ALTMSC.https://ieeexplore.ieee.org/document/11020673/Large-scale clusteringanchor-based multi-view subspace clusteringhigh-order relationshiplow-rank tensor |
| spellingShingle | Yueyao Li Yanying Mei Zhenwen Ren Bin Wu Embedded Anchors Coupled Low-Rank Tensor Learning for Multi-View Intrinsic Subspace Clustering IEEE Access Large-scale clustering anchor-based multi-view subspace clustering high-order relationship low-rank tensor |
| title | Embedded Anchors Coupled Low-Rank Tensor Learning for Multi-View Intrinsic Subspace Clustering |
| title_full | Embedded Anchors Coupled Low-Rank Tensor Learning for Multi-View Intrinsic Subspace Clustering |
| title_fullStr | Embedded Anchors Coupled Low-Rank Tensor Learning for Multi-View Intrinsic Subspace Clustering |
| title_full_unstemmed | Embedded Anchors Coupled Low-Rank Tensor Learning for Multi-View Intrinsic Subspace Clustering |
| title_short | Embedded Anchors Coupled Low-Rank Tensor Learning for Multi-View Intrinsic Subspace Clustering |
| title_sort | embedded anchors coupled low rank tensor learning for multi view intrinsic subspace clustering |
| topic | Large-scale clustering anchor-based multi-view subspace clustering high-order relationship low-rank tensor |
| url | https://ieeexplore.ieee.org/document/11020673/ |
| work_keys_str_mv | AT yueyaoli embeddedanchorscoupledlowranktensorlearningformultiviewintrinsicsubspaceclustering AT yanyingmei embeddedanchorscoupledlowranktensorlearningformultiviewintrinsicsubspaceclustering AT zhenwenren embeddedanchorscoupledlowranktensorlearningformultiviewintrinsicsubspaceclustering AT binwu embeddedanchorscoupledlowranktensorlearningformultiviewintrinsicsubspaceclustering |