fit_nonparaT {TensorComplete} | R Documentation |
Main function for nonparametric tensor estimation and completion based on low sign rank model.
Description
Estimate a signal tensor from a noisy and incomplete data tensor using nonparametric tensor method via sign series.
Usage
fit_nonparaT(Y,truer,H,Lmin,Lmax,option = 2)
Arguments
Y |
A given (possibly noisy and incomplete) data tensor. The function allows both continuous- and binary-valued tensors. Missing value should be encoded as |
truer |
Sign rank of the signal tensor. |
H |
Resolution parameter. |
Lmin |
Minimum value of the signal tensor (or minimum value of the tensor Y). |
Lmax |
Maximum value of the signal tensor (or maximum value of the tensor Y). |
option |
A large margin loss to be used. Use logistic loss if |
Value
The returned object is a list of components.
fitted
- A series of optimizers that minimize the weighted classification loss at each level.
est
- An estimated signal tensor based on nonparametic tensor method via sign series.
References
C. Lee and M. Wang. Beyond the Signs: Nonparametric Tensor Completion via Sign Series. Neural Information Processing Systems 34 (NeurIPS), 2021.
Examples
library(tensorregress)
indices = c(2,2,2)
noise = rand_tensor(indices)@data
Theta = array(runif(prod(indices),min=-1,max = 1),indices)
# The signal plus noise model
Y = Theta + noise
# Estimate Theta from nonparametic completion method via sign series
hatTheta = fit_nonparaT(Y,truer = 1,H = 1,Lmin = -1,Lmax = 1, option =2)
print(hatTheta$est)