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

A latent factor approach for prediction from multiple assays