The low-rank hurdle model

TL;DR

This paper introduces a composite loss framework for low-rank modeling of data with excess zeros or missing values, inspired by the hurdle method and generalized low-rank models, demonstrated on manufacturing data and missing value imputation.

Contribution

It presents a novel low-rank modeling approach combining composite loss functions with the hurdle method for zero-inflated data.

Findings

Effective in modeling zero-inflated data

Improves missing value imputation accuracy

Applicable to manufacturing datasets

Abstract

A composite loss framework is proposed for low-rank modeling of data consisting of interesting and common values, such as excess zeros or missing values. The methodology is motivated by the generalized low-rank framework and the hurdle method which is commonly used to analyze zero-inflated counts. The model is demonstrated on a manufacturing data set and applied to the problem of missing value imputation.