A latent factor approach for prediction from multiple assays
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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.
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