AI RESEARCH
Inductive Convolution Nuclear Norm Minimization for Tensor Completion with Arbitrary Sampling
arXiv CS.CV
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ArXi:2604.17001v1 Announce Type: new The recently established Convolution Nuclear Norm Minimization (CNNM) addresses the problem of \textit{tensor completion with arbitrary sampling} (TCAS), which involves restoring a tensor from a subset of its entries sampled in an arbitrary manner. Despite its promising performance, the optimization procedure of CNNM needs performing Singular Value Decomposition (SVD) multiple times, which is computationally expensive and hard to parallelize.