# Improving Group Lasso for high-dimensional categorical data

arXiv: https://arxiv.org/abs/2210.14021 · HTML: https://mlsanity.com/paper/2210.14021/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Cross-Validation for Correlated Data](https://mlsanity.com/paper/1904.02438/) (2019)
- [Airbnb Price Prediction Using Machine Learning and Sentiment Analysis](https://mlsanity.com/paper/1907.12665/) (2019)
- [Trees-Based Models for Correlated Data](https://mlsanity.com/paper/2102.08114/) (2021)
- [Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties](https://mlsanity.com/paper/2002.12606/) (2020)

## Abstract (arXiv)

Sparse modelling or model selection with categorical data is challenging even for a moderate number of variables, because one parameter is roughly needed to encode one category or level. The Group Lasso is a well known efficient algorithm for selection continuous or categorical variables, but all estimates related to a selected factor usually differ. Therefore, a fitted model may not be sparse, which makes the model interpretation difficult. To obtain a sparse solution of the Group Lasso we propose the following two-step procedure: first, we reduce data dimensionality using the Group Lasso; then to choose the final model we use an information criterion on a small family of models prepared by clustering levels of individual factors. We investigate selection correctness of the algorithm in a sparse high-dimensional scenario. We also test our method on synthetic as well as real datasets and show that it performs better than the state of the art algorithms with respect to the prediction accuracy or model dimension.