Exact Tensor Completion Powered by Slim Transforms
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929459646431232 |
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| author | Ge, Li Chen, Lin Chen, Yudong Jiang, Xue |
| author_facet | Ge, Li Chen, Lin Chen, Yudong Jiang, Xue |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03468 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exact Tensor Completion Powered by Slim Transforms Ge, Li Chen, Lin Chen, Yudong Jiang, Xue Machine Learning Signal Processing 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. |
| title | Exact Tensor Completion Powered by Slim Transforms |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2402.03468 |