The minimization of <inline-formula> <tex-math notation="LaTeX">$\ell _{2,p}$ </tex-math></inline-formula> norm is a powerful regularization for robustness feature extraction, sparse representation and data denoising. However, its potential for clusters distribution modeling remains largely unexplored. In this paper, w...
Guang-Yu Yang, Yu Duan, Quan-Xue Gao et al.· IEEE Transactions on Image P...· 0 citations
The proposed SGLog-γ∗-MSC, a nonconvex multi-view subspace clustering framework that combines graph total variation, an ℓ2,log penalty, and a tensor γ∗ spectral penalty, supports combining robust error modeling with local and shared structural regularization while emphasizing the dataset dependence of individual compon...
Yi Yang, Bao-Jie Pan, Ming Yang· Mathematics· 0 citations
This work proposes a novel framework termed Multi-View Graph Regularized Deep Metric Subspace Clustering (MVGR-DMSC), which consistently yields better results than several state-of-the-art approaches, including the direct baseline MSESC, in both accuracy and robustness.
Peng-Peng Luo, Ming Yang, Chong Peng et al.· PLoS ONE· 0 citations
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