Technology Category
Machine Learning: ClusteringSearch and Optimization: Mixed Discrete/Continuous SearchNatural Language Processing: Learning & Optimization for NLP
Classification EM-PCA for clustering and embedding
🤖 AI Summary
To address the curse of dimensionality and slow convergence of the Expectation-Maximization (EM) algorithm in Gaussian Mixture Model (GMM)-based clustering of high-dimensional continuous data, this paper proposes a joint embedding-and-clustering optimization framework that enables the first non-sequential, co-optimized integration of Principal Component Analysis (PCA) and Classification EM (CEM). Unlike conventional two-stage pipelines, our method simultaneously optimizes both the low-dimensional embedding space and GMM parameters, eliminating error propagation. Theoretically, it unifies PCA, K-means, CEM, and spectral clustering under a single coherent formulation. Empirical evaluation demonstrates that the proposed approach accelerates convergence by 2–5× over standard EM, improves clustering accuracy, and yields more interpretable embeddings. It consistently outperforms state-of-the-art baselines on multiple high-dimensional benchmark datasets.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Combining PCA and CEM for simultaneous clustering and embedding
Addressing slow EM convergence and high dimensionality challenges
Developing joint dimensionality reduction and clustering algorithm
Innovation
Methods, ideas, or system contributions that make the work stand out.
Combining PCA and CEM for simultaneous embedding and clustering
Using Classification EM algorithm for faster convergence
Integrating dimensionality reduction with mixture model clustering
🔎 Similar Papers
No similar papers found.
