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: 0.1.0
Imports: pracma, methods, utils, tensorregress, MASS
Published: 2021-05-11
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. 2021. https://arxiv.org/abs/2102.00384 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 NEWS
CRAN checks: TensorComplete results

Documentation:

Reference manual: TensorComplete.pdf

Downloads:

Package source: TensorComplete_0.1.0.tar.gz
Windows binaries: r-devel: TensorComplete_0.1.0.zip, r-release: TensorComplete_0.1.0.zip, r-oldrel: TensorComplete_0.1.0.zip
macOS binaries: r-release (arm64): TensorComplete_0.1.0.tgz, r-release (x86_64): TensorComplete_0.1.0.tgz, r-oldrel: TensorComplete_0.1.0.tgz

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