A latent factor approach for prediction from multiple assays

J. Kenneth Tay; Robert Tibshirani

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arXiv · 1807.05675

A latent factor approach for prediction from multiple assays

J. Kenneth Tay·Robert Tibshirani

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Abstract

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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J. Kenneth Tay, Robert Tibshirani. 2018-07-16. A latent factor approach for prediction from multiple assays. https://arxiv.org/abs/1807.05675

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