The low-rank hurdle model
This work addresses data analysis challenges in domains like manufacturing where zero-inflation or missing values are common, but it appears incremental as it combines existing generalized low-rank and hurdle methods.
The authors tackled the problem of modeling data with excess zeros or missing values by proposing a composite loss framework for low-rank modeling, and demonstrated it on a manufacturing dataset for missing value imputation.
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.