# A fast and efficient Modal EM algorithm for Gaussian mixtures

arXiv: https://arxiv.org/abs/2002.03600 · HTML: https://mlsanity.com/paper/2002.03600/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

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

- [A new semi-supervised inductive transfer learning framework: Co-Transfer](https://mlsanity.com/paper/2108.07930/) (2021)

## Abstract (arXiv)

In the modal approach to clustering, clusters are defined as the local maxima of the underlying probability density function, where the latter can be estimated either non-parametrically or using finite mixture models. Thus, clusters are closely related to certain regions around the density modes, and every cluster corresponds to a bump of the density. The Modal EM algorithm is an iterative procedure that can identify the local maxima of any density function. In this contribution, we propose a fast and efficient Modal EM algorithm to be used when the density function is estimated through a finite mixture of Gaussian distributions with parsimonious component-covariance structures. After describing the procedure, we apply the proposed Modal EM algorithm on both simulated and real data examples, showing its high flexibility in several contexts.