  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.
