Classification EM-PCA for clustering and embedding
This work addresses clustering and dimensionality reduction problems for domains like image analysis, but it appears incremental as it combines existing methods (PCA and CEM) in a non-sequential way.
The paper tackles the challenges of dimensionality and slow convergence in Gaussian mixture models for clustering by proposing a new algorithm that simultaneously performs data embedding and clustering using PCA and Classification EM. The result is improved clustering and embedding performance, though no concrete numbers are provided.
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.