  In many domains such as healthcare or finance, data often come in different
assays or measurement modalities, with features in each assay having a common
theme. Simply concatenating these assays together and performing prediction can
be effective but ignores this structure. In this setting, we propose a model
which contains latent factors specific to each assay, as well as a common
latent factor across assays. We frame our model-fitting procedure, which we
call the "Sparse Factor Method" (SFM), as an optimization problem and present
an iterative algorithm to solve it.
