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Exact Tensor Completion Powered by Slim Transforms

arXiv 2402.03468 ↗First posted 2 Feb 2024cs.LG eess.SPcited by 0

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Abstract (arXiv)

In this work, a tensor completion problem is studied, which aims to perfectly recover the tensor from partial observations. The existing theoretical guarantee requires the involved transform to be orthogonal, which hinders its applications. In this paper, jumping out of the constraints of isotropy and self-adjointness, the theoretical guarantee of exact tensor completion with arbitrary linear transforms is established by directly operating the tensors in the transform domain. With the enriched choices of transforms, a new analysis obtained by the proof discloses why slim transforms outperform their square counterparts from a theoretical level. Our model and proof greatly enhance the flexibility of tensor completion and extensive experiments validate the superiority of the proposed method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 2 Feb 2024.

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References · 8

  1. 2016Tensor Decomposition for Signal Processing and Machine Learningcited by 223
  2. 2016Tensor Ring Decompositioncited by 125
  3. 2018Tensor Robust Principal Component Analysis with A New Tensor Nuclear Normcited by 110
  4. 2015Exact tensor completion using t-SVDcited by 70
  5. 2021Tensor Methods in Computer Vision and Deep Learningcited by 52
  6. 2019Framelet Representation of Tensor Nuclear Norm for Third-Order Tensor Completioncited by 36
  7. 2020Dictionary Learning with Low-rank Coding Coefficients for Tensor Completioncited by 19
  8. 2021Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recoverycited by 16

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