Multi-view clustering has garnered significant attention for its ability to integrate heterogeneous data and uncover underlying categorical structures. However, prevailing autoencoder-based methods often yield indiscriminative embeddings, rendering them prone to trivial solutions. While diffusion models have shown prom...
Jinlin Ma, Chen-Kai Guo, Ren-Da Han et al.· Proceedings of the Thirty-Fi...· 0 citations
A novel mixed anchor strategy is introduced, which effectively bypasses the necessity of manual baseline selection while simultaneously capturing both view-specific and cross-view information and can stabilize clustering performance at a consistently high level.
Zi-Jian Chen, Miao Jia, Xing-Chen Hu et al.· Proceedings of the Thirty-Fi...· 0 citations
Multi-view clustering (MVC) relies on consistency learning to align and fuse multi-view information for building clustering decision boundaries. However, mainstream methods adopt sample-to-sample/distribution/structure similarity smoothing for consistency alignment, which builds upon continuous cluster manifolds with s...
Yuzhuo Dai, Siwei Wang, Zhibin Dong et al.· IEEE Transactions on Pattern...· 0 citations
A unified paradigm based on Cross-View Semantic Propagation (CVSP), a plug-and-play framework that effectively improves anchor quality and generalization capability when integrated with various existing methods is proposed.
Su-Yuan Liu, Si-Wei Wang, Ke Liang et al.· IEEE Transactions on Pattern...· 1 citation
Harness-G, a graph-structured retrieval framework that reformulates free-form query generation as finite action selection, and introduces Structured Non-myopic Credit (SNC), which uses a frozen answer scorer to compare the selected action with its alternatives and assigns downstream gains to the earlier actions that en...
