Multi-view Subspace Clustering
17 papers with code • 2 benchmarks • 1 datasets
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Latest papers with no code
Scalable Multi-view Clustering via Explicit Kernel Features Maps
A growing awareness of multi-view learning as an important component in data science and machine learning is a consequence of the increasing prevalence of multiple views in real-world applications, especially in the context of networks.
Multi-view Subspace Clustering via An Adaptive Consensus Graph Filter
Therefore, in the proposed method, the consensus reconstruction coefficient matrix, the consensus graph filter, and the reconstruction coefficient matrices from different views are interdependent.
Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks
In this study, contrastive multi-view subspace clustering of HSI was proposed based on graph convolutional networks.
Efficient and Effective Deep Multi-view Subspace Clustering
i) The parameter scale of the FC layer is quadratic to sample numbers, resulting in high time and memory costs that significantly degrade their feasibility in large-scale datasets.
Adaptively Topological Tensor Network for Multi-view Subspace Clustering
Therefore, a pre-defined tensor decomposition may not fully exploit low rank information for a certain dataset, resulting in sub-optimal multi-view clustering performance.
Hyper-Laplacian Regularized Concept Factorization in Low-rank Tensor Space for Multi-view Clustering
To well cope with the issues, we propose a hyper-Laplacian regularized concept factorization (HLRCF) in low-rank tensor space for multi-view clustering.
Anchor Structure Regularization Induced Multi-view Subspace Clustering via Enhanced Tensor Rank Minimization
Specifically, an anchor representation tensor is constructed by using the anchor representation strategy rather than the self-representation strategy to reduce the time complexity, and an Anchor Structure Regularization (ASR) is employed to enhance the local geometric structure in the learned anchor-representation tensor.
Double Graphs Regularized Multi-view Subspace Clustering
In this paper, we propose a novel Double Graphs Regularized Multi-view Subspace Clustering (DGRMSC) method, which aims to harness both global and local structural information of multi-view data in a unified framework.
Enriched Robust Multi-View Kernel Subspace Clustering
To address the above issues, in this paper we propose a novel Enriched Robust Multi-View Kernel Subspace Clustering framework where the consensus affinity matrix is learned from both multi-view data and spectral clustering.
Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering
Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representations of other views.
