Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm.
IEEE Transactions on Pattern Analysis and Machine Intelligence(2020)
摘要
In this paper, we consider the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum. Our model is based on the recently proposed tensor-tensor product (or t-product) [14]. Induced by the t-product, we first rigorously deduce the tensor spectral norm, tensor nuclear norm, and tensor average rank, and show that th...
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关键词
Principal component analysis,Sparse matrices,Matrix decomposition,Numerical models,Noise measurement,Convex functions
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