Ordered homogeneity pursuit lasso for group variable selection with applications to spectroscopic data

- A novel group variable selection method named ordered homogeneity pursuit lasso (OHPL) is proposed.
- OHPL takes the homogeneity structure in high-dimensional data into account and is completely data-driven.
- OHPL shows better prediction performance than state-of-the-art variable selection methods on real-world spectroscopic data.

In high-dimensional data modeling, variable selection methods have been a popular choice to improve the prediction accuracy by effectively selecting the subset of informative variables, and such methods can enhance the model interpretability with sparse representation. In this study, we propose a novel group variable selection method named ordered homogeneity pursuit lasso (OHPL) that takes the homogeneity structure in high-dimensional data into account. OHPL is particularly useful in high-dimensional datasets with strongly correlated variables. We illustrate the approach using three real-world spectroscopic datasets and compare it with four state-of-the-art variable selection methods. The benchmark results on real-world data show that the proposed method is capable of identifying a small number of influential groups and has better prediction performance than its competitors. The OHPL method and the spectroscopic datasets are implemented and included in an R package OHPL available from https://ohpl.io.