TL;DR
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.
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.
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...
Graph-based multi-view clustering, with its ability to mine potential associations between samples, has attracted extensive attention. To capture high-order correlations, tensor-based frameworks have been introduced to model multiple graphs jointly. Although these methods have achieved promising performance, existing m...
An innovative MVC method utilizing orthogonal non-negative anchor tensor factorization, termed ATFMC, which employs a shared-nearest-neighbor density peaks clustering algorithm, which integrates cross-view features to select high-quality anchors and achieves superior clustering performance.
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.
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