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Found 7 papers, 4 papers with code
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.
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views.
Scalable Incomplete Multi-View Clustering with Structure Alignment
Although several anchor-based IMVC methods have been proposed to process the large-scale incomplete data, they still suffer from the following drawbacks: i) Most existing approaches neglect the inter-view discrepancy and enforce cross-view representation to be consistent, which would corrupt the representation capability of the model; ii) Due to the samples disparity between different views, the learned anchor might be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete data (AUP-ID).
Unpaired Multi-View Graph Clustering with Cross-View Structure Matching
Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering.
One-step Multi-view Clustering with Diverse Representation
In light of this, we propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and $k$-means into a unified framework.
Fast Continual Multi-View Clustering with Incomplete Views
Such an incomplete continual data problem (ICDP) in MVC is tough to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views.
Auto-weighted Multi-view Clustering for Large-scale Data
Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views.
