# Primal path algorithm for compositional data analysis

arXiv: https://arxiv.org/abs/1812.08954 · HTML: https://mlsanity.com/paper/1812.08954/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [CLASSO: a practical primal path-following algorithm for the constrained LASSO](https://mlsanity.com/paper/2610.17024/) (2026)

## Abstract (arXiv)

Compositional data have two unique characteristics compared to typical multivariate data: the observed values are nonnegative and their summand is exactly one. To reflect these characteristics, a specific regularized regression model with linear constraints is commonly used. However, linear constraints incur additional computational time, which becomes severe in high-dimensional cases. As such, we propose an efficient solution path algorithm for a l1 regularized regression with compositional data. The algorithm is then extended to a classification model with compositional predictors. We also compare its computational speed with that of previously developed algorithms and apply the proposed algorithm to analyze human gut microbiome data.