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Found 7 papers, 0 papers with code
One for all: A novel Dual-space Co-training baseline for Large-scale Multi-View Clustering
We jointly optimize the construction of the latent consistent anchor graph and the feature transformation to generate a discriminative anchor graph.
Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data
Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields.
ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering
In this paper, we propose an augmentation-free graph contrastive learning framework, namely ACTIVE, to solve the problem of partial multi-view clustering.
Incomplete Multi-view Clustering via Cross-view Relation Transfer
In this paper, we consider the problem of multi-view clustering on incomplete views.
Double Low-Rank Representation With Projection Distance Penalty for Clustering
This paper presents a novel, simple yet robust self-representation method, i. e., Double Low-Rank Representation with Projection Distance penalty (DLRRPD) for clustering.
Consistent Multiple Graph Embedding for Multi-View Clustering
Specifically, a multiple graph auto-encoder(M-GAE) is designed to flexibly encode the complementary information of multi-view data using a multi-graph attention fusion encoder.
Auto-weighted low-rank representation for clustering
In this paper, a novel unsupervised low-rank representation model, i. e., Auto-weighted Low-Rank Representation (ALRR), is proposed to construct a more favorable similarity graph (SG) for clustering.
