Multi-task Learning for Compositional Data via Sparse Network Lasso

TL;DR

This paper introduces a novel multi-task learning method tailored for compositional data using sparse network lasso, addressing limitations of existing methods and demonstrating effectiveness through simulations and microbiome data analysis.

Contribution

It develops a sparse network lasso-based multi-task learning approach specifically for compositional data, leveraging the symmetric log-contrast model.

Findings

Effective in simulation studies

Successful application to gut microbiome data

Addresses limitations of existing multi-task methods

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.