RIP-based performance guarantee for low-tubal-rank tensor recovery
نویسندگان
چکیده
منابع مشابه
Provable Low-Rank Tensor Recovery
In this paper, we rigorously study tractable models for provably recovering low-rank tensors. Unlike their matrix-based predecessors, current convex approaches for recovering low-rank tensors based on incomplete (tensor completion) and/or grossly corrupted (tensor robust principal analysis) observations still suffer from the lack of theoretical guarantees, although they have been used in variou...
متن کاملLow-tubal-rank Tensor Completion using Alternating Minimization
The low-tubal-rank tensor model has been recently proposed for real-world multidimensional data. In this paper, we study the low-tubal-rank tensor completion problem, i.e., to recover a third-order tensor by observing a subset of its elements selected uniformly at random. We propose a fast iterative algorithm, called Tubal-AltMin, that is inspired by a similar approach for low-rank matrix compl...
متن کاملTensor theta norms and low rank recovery
We study extensions of compressive sensing and low rank matrix recovery to the recovery of tensors of low rank from incomplete linear information. While the reconstruction of low rank matrices via nuclear norm minimization is rather well-understand by now, almost no theory is available so far for the extension to higher order tensors due to various theoretical and computational difficulties ari...
متن کاملRecovery guarantee of weighted low-rank approximation via alternating minimization
Many applications require recovering a ground truth low-rank matrix from noisy observations of the entries. In practice, this is typically formulated as a weighted low-rank approximation problem and solved using non-convex optimization heuristics such as alternating minimization. Such non-convex techniques have few guarantees. Even worse, weighted low-rank approximation is NP-hard for even the ...
متن کاملWeighted Low-rank Tensor Recovery for Hyperspectral Image Restoration
Hyperspectral imaging, providing abundant spatial and spectral information simultaneously, has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to various degradations, such noises (random noise, HSI denoising), blurs (Gaussian and uniform blur, HSI deblurring), and down-sampled (both spectral and spatial do...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computational and Applied Mathematics
سال: 2020
ISSN: 0377-0427
DOI: 10.1016/j.cam.2020.112767