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Sparse and Low-rank Tensor Estimation via Cubic Sketchings
In this paper, we propose a general framework for sparse and low-rank tensor estimation from cubic sketchings. A two-stage non-convex implementation is developed based on sparse tensor decomposition and thresholded gradient descent, which ensures exact recovery in the noiseless case and stable recov...
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| Publié dans: | IEEE Trans Inf Theory |
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| Auteurs principaux: | , , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
2020
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7978041/ https://ncbi.nlm.nih.gov/pubmed/33746244 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/tit.2020.2982499 |
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