Multi-view Subspace Clustering
17 papers with code • 2 benchmarks • 1 datasets
ORL
Latest papers
Enhanced Latent Multi-view Subspace Clustering
Specifically, we stack the data matrices from various views into the block-diagonal locations of the augmented matrix to exploit the complementary information.
Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent
However, we propose Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) to tackle the discrepancy among views through hierarchical feature descent and project to a common subspace( STAGE 1), which reveals dependency of different views.
Multi-view MERA Subspace Clustering
Benefiting from multiple interactions among orthogonal/semi-orthogonal (low-rank) factors, the low-rank MERA has a strong representation power to capture the complex inter/intra-view information in the self-representation tensor.
Deep Multi-View Subspace Clustering with Anchor Graph
gzcch/DMCAG
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To significantly reduce the complexity, we construct an anchor graph with small size for each view.
Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering
Second, many of them overlook the local structures of multiple views and cannot jointly leverage multiple local structures to enhance the subspace representation learning.
High-order Correlation Preserved Incomplete Multi-view Subspace Clustering
Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a thirdorder low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation.
Fine-grained Graph Learning for Multi-view Subspace Clustering
To utilize the multi-view information sufficiently, we design a specific graph learning method by introducing graph regularization and a local structure fusion pattern.
Smoothed Multi-View Subspace Clustering
In recent years, multi-view subspace clustering has achieved impressive performance due to the exploitation of complementary imformation across multiple views.
Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat
However, different views often have distinct incompleteness, i. e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views).
Multi-view Subspace Clustering Networks with Local and Global Graph Information
ZQH92/MSCNLG
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Furthermore, underlying graph information of multi-view data is always ignored in most existing multi-view subspace clustering methods.
