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Found 5 papers, 1 papers with code
Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters
To this end, we introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data, which seamlessly integrates information from different views without the need for a predefined number of clusters.
Contrastive Continual Multi-view Clustering with Filtered Structural Fusion
Precisely, considering that data correlations play a vital role in clustering and prior knowledge ought to guide the clustering process of a new view, we develop a data buffer with fixed size to store filtered structural information and utilize it to guide the generation of a robust partition matrix via contrastive learning.
Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment
The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views.
Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering
However, most existing deep clustering approaches are dedicated to merging and exploring the consistent latent representation across multiple views while overlooking the abundant complementary information in each view.
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View
However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance.
