This paper studies the Tensor Robust Principal Component (TRPCA) problem which extends the known Robust P-CA [4] to the tensor case. Our model is based on a new tensor Singular Value Decomposition (t-SVD) [14] and its induced tensor tubal rank and tensor nuclear norm. Consider that we have a 3-way tensor X ∈ R n 1 ×n 2 ×n 3 such that X = L 0 + S 0 , where L 0 has low tubal rank and S 0 is sparse. Is that possible to recover both components? In this work, we prove that under certain suitable assumptions, we can recover both the low-rank and the sparse components exactly by simply solving a convex program whose objective is a weighted combination of the tensor nuclear norm and the 1 -norm, i.e., where λ = 1/ max(n 1 , n 2 )n 3 . Interestingly, TRPCA involves RPCA as a special case when n 3 = 1 and thus it is a simple and elegant tensor extension of RPCA. Also numerical experiments verify our theory and the application for the image denoising demonstrates the effectiveness of our method.
Lu et al. REF extended the work and studied the Tensor Robust Principal Component (TRPCA) problem, as defined in the following equation:
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science Mathematics