Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
476532 | Egyptian Informatics Journal | 2011 | 12 Pages |
Abstract
Finite mixture models (FMM) is a well-known pattern recognition method, in which parameters are commonly determined from complete data using the Expectation Maximization (EM) algorithm. In this paper, a new algorithm is proposed to determine FMM parameters from incomplete data. Compared with a modified EM algorithm that is proposed earlier the proposed algorithm has better performance than the modified EM algorithm when the dimensions containing missing values are at least moderately correlated with some of the complete dimensions.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Ahmed R. Abas,