# Variable Selection Methods for Model-based Clustering

arXiv: https://arxiv.org/abs/1707.00306 · HTML: https://mlsanity.com/paper/1707.00306/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2017.

## Built on by (most cited first, 7 of 7)

- [Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection](https://mlsanity.com/paper/2302.03391/) (2023)
- [VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data](https://mlsanity.com/paper/2406.16227/) (2024)
- [Unobserved classes and extra variables in high-dimensional discriminant analysis](https://mlsanity.com/paper/2102.01982/) (2021)
- [Bayesian outcome-guided multi-view mixture models with applications in molecular precision medicine](https://mlsanity.com/paper/2303.00318/) (2023)
- [DiscoVars: A New Data Analysis Perspective -- Application in Variable Selection for Clustering](https://mlsanity.com/paper/2304.03983/) (2023)
- [A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images](https://mlsanity.com/paper/2403.03349/) (2024)
- [Fast estimation of Gaussian mixture components via centering and singular value thresholding](https://mlsanity.com/paper/2604.19091/) (2026)

## Abstract (arXiv)

Model-based clustering is a popular approach for clustering multivariate data which has seen applications in numerous fields. Nowadays, high-dimensional data are more and more common and the model-based clustering approach has adapted to deal with the increasing dimensionality. In particular, the development of variable selection techniques has received a lot of attention and research effort in recent years. Even for small size problems, variable selection has been advocated to facilitate the interpretation of the clustering results. This review provides a summary of the methods developed for variable selection in model-based clustering. Existing R packages implementing the different methods are indicated and illustrated in application to two data analysis examples.