TensorComplete: Tensor Noise Reduction and Completion Methods (original) (raw)
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.htmlChanwoo 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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