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arXiv · 2111.06617

Multi-task Learning for Compositional Data via Sparse Network Lasso

Akira Okazaki·Shuichi Kawano
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Abstract

A network lasso enables us to construct a model for each sample, which is known as multi-task learning. Existing methods for multi-task learning cannot be applied to compositional data due to their intrinsic properties. In this paper, we propose a multi-task learning method for compositional data using a sparse network lasso. We focus on a symmetric form of the log-contrast model, which is a regression model with compositional covariates. The effectiveness of the proposed method is shown through simulation studies and application to gut microbiome data.

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Akira Okazaki, Shuichi Kawano. 2021-11-12. Multi-task Learning for Compositional Data via Sparse Network Lasso. https://doi.org/10.3390/e24121839

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