Classification EM-PCA for clustering and embedding

TL;DR

This paper introduces a novel algorithm combining PCA and Classification EM to improve clustering and data embedding efficiency, addressing issues of high dimensionality and slow convergence in Gaussian mixture models.

Contribution

The paper proposes a combined PCA and CEM algorithm for simultaneous data embedding and clustering, enhancing speed and handling high-dimensional data effectively.

Findings

Improved clustering accuracy demonstrated.

Faster convergence compared to traditional EM.

Effective dimensionality reduction achieved.

Abstract

The mixture model is undoubtedly one of the greatest contributions to clustering. For continuous data, Gaussian models are often used and the Expectation-Maximization (EM) algorithm is particularly suitable for estimating parameters from which clustering is inferred. If these models are particularly popular in various domains including image clustering, they however suffer from the dimensionality and also from the slowness of convergence of the EM algorithm. However, the Classification EM (CEM) algorithm, a classifying version, offers a fast convergence solution while dimensionality reduction still remains a challenge. Thus we propose in this paper an algorithm combining simultaneously and non-sequentially the two tasks --Data embedding and Clustering-- relying on Principal Component Analysis (PCA) and CEM. We demonstrate the interest of such approach in terms of clustering and data embedding. We also establish different connections with other clustering approaches.