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.
- Version0.2.0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Chanwoo Lee and Miaoyan Wang. Tensor denoising and completion based on ordinal observations. ICML, 2020.
- Chanwoo Lee and Miaoyan Wang. Beyond the Signs: Nonparametric tensor completion via sign series. NeurIPS, 2021.
- Chanwoo Lee, Lexin Li, Hao Helen Zhang, and Miaoyan Wang. Nonparametric trace regression in high dimensions via sign series representation. 2021.
- Last release04/14/2023
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Team
Chanwoo Lee
Miaoyan Wang
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- Imports3 packages