fit_continuous_tucker: Signal tensor estimation from a noisy and incomplete data...

In TensorComplete: Tensor Noise Reduction and Completion Methods

Signal tensor estimation from a noisy and incomplete data tensor based on the Tucker model.

Description

Estimate a signal tensor from a noisy and incomplete data tensor using the Tucker model.

Usage

fit_continuous_tucker(ttnsr,r,alpha = TRUE)

Arguments

ttnsr	A given (possibly noisy and incomplete) data tensor.
r	A rank to be fitted (Tucker rank).
alpha	A signal level alpha = TRUE If the signal level is unknown.

Value

A list containing the following:

C - An estimated core tensor.

A - Estimated factor matrices.

iteration - The number of iterations.

cost - Log-likelihood value at each iteration.

Examples

# Latent parameters
library(tensorregress)
alpha = 10
A_1 = matrix(runif(10*2,min=-1,max=1),nrow = 10)
A_2 = matrix(runif(10*2,min=-1,max=1),nrow = 10)
A_3 = matrix(runif(10*2,min=-1,max=1),nrow = 10)
C = as.tensor(array(runif(2^3,min=-1,max=1),dim = c(2,2,2)))
theta = ttm(ttm(ttm(C,A_1,1),A_2,2),A_3,3)@data
theta = alpha*theta/max(abs(theta))
adj = mean(theta)
theta = theta-adj
omega = c(-0.2,0.2)+adj

# Observed tensor
ttnsr <- realization(theta,omega)@data

# Estimation of parameters
continuous_est = fit_continuous_tucker(ttnsr,c(2,2,2),alpha = 10)

TensorComplete documentation built on April 14, 2023, 9:10 a.m.