Simultaneous model-based clustering and visualization in the Fisher discriminative subspace

TL;DR

This paper introduces a discriminative latent mixture model that performs simultaneous clustering and visualization in a low-dimensional discriminative subspace, improving accuracy and interpretability in high-dimensional data analysis.

Contribution

It proposes the DLM model and Fisher-EM algorithm for effective clustering and visualization in a lower-dimensional discriminative space, adaptable to various data situations.

Findings

Outperforms existing clustering methods on simulated and real datasets.

Provides a meaningful low-dimensional representation of clustered data.

Successfully applied to mass spectrometry data clustering.

Abstract

Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult task from both the clustering accuracy and the result understanding points of view. This paper presents a discriminative latent mixture (DLM) model which fits the data in a latent orthonormal discriminative subspace with an intrinsic dimension lower than the dimension of the original space. By constraining model parameters within and between groups, a family of 12 parsimonious DLM models is exhibited which allows to fit onto various situations. An estimation algorithm, called the Fisher-EM algorithm, is also proposed for estimating both the mixture parameters and the discriminative subspace. Experiments on simulated and real datasets show that the proposed approach performs better than existing clustering methods while providing a useful representation of the clustered data. The method is as well applied to the clustering of mass spectrometry data.