Robust High-Order Tensor Recovery via Nonconvex Low-Rank Approximation
Wenjin Qin, Jianjun Wang, Hailin Wang, Weijun Ma
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SPS
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The latest tensor recovery methods based on tensor Singular Value Decomposition (t-SVD) mainly utilize the tensor nuclear norm (TNN) as a convex surrogate of the rank function. However, TNN minimization treats each rank component equally and tends to over-shrink the dominant ones, thereby usually leading to biased solutions. To handle this critical issue, we put forward a weighted tensor Schantten-$p$ ($0