An Accelerated Algorithm for Density Estimation in Large Databases Using Gaussian Mixtures

Today, with the advances of computer storage and technology, there are huge datasets available, offering an opportunity to extract valuable information. Probabilistic approaches are specially suited to learn from data by representing knowledge as density functions. In this paper, we choose Gaussian Mixture Models (GMMs) to represent densities, as they possess great flexibility to adequate to a wide class of problems. The classical estimation approach for GMMs corresponds to the iterative algorithm of Expectation Maximization. This approach, however, does not scale properly to meet the high demanding processing requirements of large databases. In this paper we introduce an EM-based algorithm, that solves the scalability problem. Our approach is based on the concept 1 

• Type: Journal
• Year: 2007
• Where: CAS
• Authors: Alvaro Soto, Felipe Zavala, Anita Araneda
• Keywords: CAS 2007, CAS 2008, Classical Estimation Approach, Gaussian Mixture Models, Huge Datasets