fit_ordinal: Fitting the cumulative logistic model to an ordinal tensor

In tensorordinal: Tensor Noise Reduction and Completion Based on Ordinal Observations

Description

Fit the cumulative logistic model to an ordinal data tensor

Usage

fit_ordinal(ttnsr,r,omega=TRUE,alpha = TRUE)

Arguments

ttnsr	an observed tensor
r	a rank to be fitted (Tucker rank)
omega	the cut-off points if known, omega = TRUE if unknown
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

theta - an estimated latent parameter tensor

iteration - the number of iterations

cost - log-likelihood value at each iteration

omega - estimated cut-off points

References

Lee, C., & Wang, M. (2020). Tensor denoising and completion based on ordinal observations. arXiv preprint arXiv:2002.06524.

Examples

# Latent parameters
library(rTensor)
alpha = 10
A_1 = matrix(runif(15*2,min=-1,max=1),nrow = 15)
A_2 = matrix(runif(15*2,min=-1,max=1),nrow = 15)
A_3 = matrix(runif(15*2,min=-1,max=1),nrow = 15)
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
ordinal_est = fit_ordinal(ttnsr,c(2,2,2),omega = TRUE,alpha = 10)

tensorordinal documentation built on Jan. 13, 2021, 9:17 p.m.