likelihood

ttnsr

a continuous-valued tensor (latent parameters)

theta

omega

types of log-likelihood function<code>"ordinal"</code> specifies log-likelihood function based on the cumulative logistic model<code>"Gaussian"</code> specifies log-likelihood function based on the Gaussian model

type

Return log-likelihood function (cost function) value evaluated at a given parameter tensor, an observed tensor, and cut-off points

Efficient algorithm for tensor noise reduction and completion of ordinal tensor data based on the cumulative link model. The algorithm employs the alternating optimization approach. The detailed algorithm description can be found in Lee and Wang, Proceedings of International Conference on Machine Learning, 119:5778-5788, 2020.

Chanwoo Lee

tensorordinal

Tensor Noise Reduction and Completion Based on Ordinal
Observations

likelihood function

types of log-likelihood function

<code>"ordinal"</code> specifies log-likelihood function based on the cumulative logistic model

<code>"Gaussian"</code> specifies log-likelihood function based on the Gaussian model

likelihood: Log-likelihood function (cost function)

Description

Usage

Arguments

Value