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.


Reference manual

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0.1.0 by Chanwoo Lee, 5 months ago

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

Browse source code at https://github.com/cran/TensorComplete

Authors: Chanwoo Lee <[email protected]> , Miaoyan Wang <[email protected]>

Documentation:   PDF Manual  

GPL (>= 2) license

Imports pracma, methods, utils, tensorregress, MASS

See at CRAN