meet: Implements the Modified eigenvalues estimator for Tucker rank...
In dcgerard/hose: Higher-order spectral estimators
Description
Usage
Arguments
Value
Author(s)
References
View source: R/cichocki_meet.R
Description
The model is
Y = A + E,
where A is a low-rank matrix and E is
the noise matrix is i.i.d. N(0,s) entries. s is not assumed to be known.
Usage
1
Arguments
Y
A numeric array.
tau2
The known variance. Will be estimated if missing. Not supported yet.
Value
A list with two elements: rank the estimated multilinear rank of the tensor and
sigma a vector of the estimated variance when using each mode.
Author(s)
David Gerard
References
Yokota, Tatsuya, Namgil Lee, and Andrzej Cichocki. "Robust multilinear tensor rank estimation using higher order singular value decomposition and information criteria." IEEE Transactions on Signal Processing 65.5 (2017): 1196-1206. DOI: 10.1109/TSP.2016.2620965
dcgerard/hose documentation built on Aug. 1, 2019, 12:11 a.m.
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Description Usage Arguments Value Author(s) References
View source: R/cichocki_meet.R
Description
The model is
Y = A + E,
where A is a low-rank matrix and E is the noise matrix is i.i.d. N(0,s) entries. s is not assumed to be known.
Usage
1 |
Arguments
Y |
A numeric array. |
tau2 |
The known variance. Will be estimated if missing. Not supported yet. |
Value
A list with two elements: rank the estimated multilinear rank of the tensor and
sigma a vector of the estimated variance when using each mode.
Author(s)
David Gerard
References
Yokota, Tatsuya, Namgil Lee, and Andrzej Cichocki. "Robust multilinear tensor rank estimation using higher order singular value decomposition and information criteria." IEEE Transactions on Signal Processing 65.5 (2017): 1196-1206. DOI: 10.1109/TSP.2016.2620965
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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