A fast and efficient Modal EM algorithm for Gaussian mixtures
This provides an incremental improvement for researchers in statistical clustering by speeding up mode detection in Gaussian mixtures.
The authors tackled the problem of identifying clusters as local maxima of Gaussian mixture densities by proposing a fast and efficient Modal EM algorithm, demonstrating its high flexibility in simulated and real data contexts.
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.