Author

Feng Zhang

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Preprint Oct 2026

Robust Tensor Completion via Reflective Convolution Nuclear Norm Minimization

Robust tensor completion recovers multidimensional data from partial observations corrupted by sparse gross errors. Existing convolutional low-rank models typically construct translated copies using circular continuation, which introduces artificial wrap-around neighborhoods for finite nonperiodic data. We propose refl...

Wei-Guo Zhou, Feng Zhang, Wen-Jin Qin et al. · 0 citations

Preprint Sep 2026

Robust low-rank tensor completion via factorized weighted tensor schatten-p norm minimization

Two weighted Schatten-$p tensor factorization models are proposed under the tensor-tensor product (t-product) framework to address limitations of low-rank tensor factorization and demonstrate competitive reconstruction quality and robustness under various degradation conditions.

Bing-Hao Wang, Feng Zhang, Wen-Dong Wang et al. · 0 citations

#machine learning Preprint Sep 2026

Semi-Tensor Product-Based Multi-Term Randomized T-SVD and Its Visual Applications

A novel semi-tensor product for third-order tensors under the t-product framework induced by arbitrary invertible linear transforms is introduced, yielding an accelerated multi-term randomized semi-tensor product SVD algorithm that achieves a balance between reconstruction accuracy and computational efficiency.

Xing-Chen Xiao, Feng Zhang, Wen-Jin Qin et al. · 0 citations