Dimension reduction for model-based clustering

TL;DR

This paper presents a dimension reduction technique tailored for visualizing clustering structures in high-dimensional data using Gaussian mixture models, enhancing interpretability and noise robustness.

Contribution

It introduces a novel linear combination-based dimension reduction method that captures most clustering information from Gaussian mixture models for improved visualization.

Findings

Effective in high-dimensional settings

Produces clear visual summaries of clusters

Applicable to both simulated and real data

Abstract

We introduce a dimension reduction method for visualizing the clustering structure obtained from a finite mixture of Gaussian densities. Information on the dimension reduction subspace is obtained from the variation on group means and, depending on the estimated mixture model, on the variation on group covariances. The proposed method aims at reducing the dimensionality by identifying a set of linear combinations, ordered by importance as quantified by the associated eigenvalues, of the original features which capture most of the cluster structure contained in the data. Observations may then be projected onto such a reduced subspace, thus providing summary plots which help to visualize the clustering structure. These plots can be particularly appealing in the case of high-dimensional data and noisy structure. The new constructed variables capture most of the clustering information available in the data, and they can be further reduced to improve clustering performance. We illustrate the approach on both simulated and real data sets.