Search Results for author:
Found 6 papers, 3 papers with code
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.
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.
One-Pass Multi-View Clustering for Large-Scale Data
In this way, the generated partition can guide multi-view matrix factorization to produce more purposive coefficient matrix which, as a feedback, improves the quality of partition.
Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix
Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data by performing clustering on the learned optimal embedding across views.
