TL;DR
A robust multi-view subspace clustering method with enhanced higher-order correlations, termed RMVSC-EHOC, which introduces Laplacian regularization and robust principal component analysis to learn stable latent representations while preserving local neighborhood structure.
This paper proposes salient-residual decoupled multi-view learning for clustering, SRDMVC, introducing a novel decomposition-fusion iterative optimization, which separates the feature space into a salient space and a residual subspace effectively and fuses them using a novel attention mechanism.
A Correntropy-guided Matrix Factorization and Tensor Graph Learning (CMTGL) framework for robust semi-supervised multi-view clustering that incorporates partial label information into a shared low-dimensional representation through constrained low-rank matrix factorization and employs the maximum correntropy criterion...
The proposed one-step Tensor Low-Frequency learning for Incomplete Multi-View Clustering (TLF-IMVC) jointly models low-frequency and low-rank structures on stacked spectral embedding tensors to enable efficient and consistent cross-view information fusion.
Multi-view Clustering via Manifold Decomposition is proposed, which directly infers cluster labels from multi-view data without explicit similarity graph construction or anchor selection, and reformulates multi-view clustering as a unified multi-view regression problem, where cluster labels are optimized as model varia...
A pairwise co-regularization mechanism is introduced to capture cross-view structural correlations by measuring the similarity between view-specific coefficient matrices and a stochastic acceleration strategy is incorporated to expedite convergence.
A flexible encoder based on low-rank embedding that adaptively captures the most informative components in the feature space of the input data while filtering out redundant or noisy information, effectively alleviating over-connected affinities is proposed.
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