Sciweavers

CAS
2007

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

13 years 9 months ago
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
Alvaro Soto, Felipe Zavala, Anita Araneda
Added 18 Dec 2010
Updated 18 Dec 2010
Type Journal
Year 2007
Where CAS
Authors Alvaro Soto, Felipe Zavala, Anita Araneda
Comments (0)