tensorordinal: Tensor Noise Reduction and Completion Based on Ordinal Observations

Efficient algorithm for tensor noise reduction and completion of ordinal tensor data based on the cumulative link model. The algorithm employs the alternating optimization approach. The detailed algorithm description can be found in Lee and Wang, Proceedings of International Conference on Machine Learning, 119:5778-5788, 2020.

Version: 0.2.0
Imports: pracma, MASS, rTensor, methods
Published: 2020-12-13
Author: Chanwoo Lee, Miaoyan Wang
Maintainer: Chanwoo Lee <chanwoo.lee at wisc.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://proceedings.mlr.press/v119/lee20i.html
NeedsCompilation: no
Materials: README NEWS
CRAN checks: tensorordinal results

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Reference manual: tensorordinal.pdf
Package source: tensorordinal_0.2.0.tar.gz
Windows binaries: r-devel: tensorordinal_0.2.0.zip, r-release: tensorordinal_0.2.0.zip, r-oldrel: tensorordinal_0.2.0.zip
macOS binaries: r-release: tensorordinal_0.2.0.tgz, r-oldrel: tensorordinal_0.2.0.tgz
Old sources: tensorordinal archive

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