Multi-view Subspace Clustering
14 papers with code • 2 benchmarks • 1 datasets
Most implemented papers
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.
Multi-view Low-rank Sparse Subspace Clustering
Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral clustering algorithm to handle multi-view data.
Large-scale Multi-view Subspace Clustering in Linear Time
A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years.
Deep Multimodal Subspace Clustering Networks
In addition to various spatial fusion-based methods, an affinity fusion-based network is also proposed in which the self-expressive layer corresponding to different modalities is enforced to be the same.
Feature Concatenation Multi-view Subspace Clustering
To this end, this paper proposes a novel multi-view subspace clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC), which boosts the clustering performance by exploring the consensus information of multi-view data.
Constrained Bilinear Factorization Multi-view Subspace Clustering
Multi-view clustering is an important and fundamental problem.
Multi-view Deep Subspace Clustering Networks
2) The end-to-end learning manner of deep learning is not well used in multi-view clustering.
Adaptive multi-view subspace clustering for high-dimensional data,
With the rapid development of multimedia technologies, we frequently confront with high-dimensional data and multi-view data, which usually contain redundant features and distinct types of features.
Multi-view Subspace Clustering Networks with Local and Global Graph Information
Furthermore, underlying graph information of multi-view data is always ignored in most existing multi-view subspace clustering methods.
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).