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

Documentation:

Reference manual:

TensorComplete.pdf

Downloads:

Package source:	TensorComplete_0.2.0.tar.gz
Windows binaries:	r-devel: TensorComplete_0.2.0.zip, r-release: TensorComplete_0.2.0.zip, r-oldrel: TensorComplete_0.2.0.zip
macOS binaries:	r-release (arm64): TensorComplete_0.2.0.tgz, r-oldrel (arm64): TensorComplete_0.2.0.tgz, r-release (x86_64): TensorComplete_0.2.0.tgz, r-oldrel (x86_64): TensorComplete_0.2.0.tgz
Old sources:	TensorComplete archive

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