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Found 5 papers, 3 papers with code
Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding
In this work, we proposed a novel Incomplete Contrastive Multi-View Clustering method with high-confidence guiding (ICMVC).
Disentangling Multi-view Representations Beyond Inductive Bias
In this paper, we propose a novel multi-view representation disentangling method that aims to go beyond inductive biases, ensuring both interpretability and generalizability of the resulting representations.
A Clustering-guided Contrastive Fusion for Multi-view Representation Learning
To this end, we utilize a deep fusion network to fuse view-specific representations into the view-common representation, extracting high-level semantics for obtaining robust representation.
Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk
We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
A Survey on Multi-View Clustering
With advances in information acquisition technologies, multi-view data become ubiquitous.
