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TensorComplete: Tensor Noise Reduction and Completion Methods
Efficient algorithms for tensor noise reduction and completion. This package includes a suite of parametric and nonparametric tools for estimating tensor signals from noisy, possibly incomplete observations. The methods allow a broad range of data types, including continuous, binary, and ordinal-valued tensor entries. The algorithms employ the alternating optimization. The detailed algorithm description can be found in the following three references.
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Package details |
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| Author | Chanwoo Lee <chanwoo.lee@wisc.edu>, Miaoyan Wang <miaoyan.wang@wisc.edu> |
| Maintainer | Chanwoo Lee <chanwoo.lee@wisc.edu> |
| License | GPL (>= 2) |
| Version | 0.2.0 |
| URL | Chanwoo Lee and Miaoyan Wang. Tensor denoising and completion based on ordinal observations. ICML 2020. http://proceedings.mlr.press/v119/lee20i.html Chanwoo Lee and Miaoyan Wang. Beyond the Signs: Nonparametric tensor completion via sign series. NeurIPS 2021. https://papers.nips.cc/paper/2021/hash/b60c5ab647a27045b462934977ccad9a-Abstract.html Chanwoo Lee Lexin Li Hao Helen Zhang and Miaoyan Wang. Nonparametric trace regression in high dimensions via sign series representation. 2021. https://arxiv.org/abs/2105.01783 |
| Package repository | View on CRAN |
| Installation |
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