A latent factor approach for prediction from multiple assays
Methodology
2018-07-17 v1
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.
Cite
@article{arxiv.1807.05675,
title = {A latent factor approach for prediction from multiple assays},
author = {J. Kenneth Tay and Robert Tibshirani},
journal= {arXiv preprint arXiv:1807.05675},
year = {2018}
}