Summary of Exact Tensor Completion Powered by Slim Transforms, By Li Ge et al.
Exact Tensor Completion Powered by Slim Transforms
by Li Ge, Lin Chen, Yudong Chen, Xue Jiang
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper tackles the challenge of recovering tensors from partial observations, aiming to perfectly complete the tensor. The existing guarantees require orthogonal transforms, which limits their applications. To overcome this limitation, the authors establish a new theoretical guarantee for exact tensor completion using arbitrary linear transforms. By operating in the transform domain, they show that slim transforms outperform square counterparts from a theoretical perspective. This breakthrough enhances the flexibility of tensor completion and is validated through extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to recover incomplete tensors. Imagine having a puzzle with some pieces missing – you want to fill them in perfectly. Current methods require special “transforms” that are like specific directions for putting the puzzle together. The authors showed that these transforms don’t have to be special, and they can use any linear transform instead. This makes it easier to complete tensors and leads to better results. |
