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.
| Version: |
0.2.0 |
| Imports: |
pracma, methods, utils, tensorregress, MASS |
| Published: |
2023-04-14 |
| DOI: |
10.32614/CRAN.package.TensorComplete |
| Author: |
Chanwoo Lee, Miaoyan Wang |
| Maintainer: |
Chanwoo Lee <chanwoo.lee at wisc.edu> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| 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 |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
TensorComplete results |
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