Incremental general regression with expectation maximization for learning finite mixtures using data with missing values

Author

Abas, Ahmed R.

Author_Institution

Dept. of Comput. Sci., Umm Al-Qura Univ., Makka Al-Mukarrama, Saudi Arabia

fYear

2013

fDate

22-24 June 2013

Firstpage

1

Lastpage

6

Abstract

Finite mixture models (FMM) is a pattern recognition method, in which parameters are determined from complete data using the Expectation Maximization (EM) algorithm. This paper presents an algorithm for determining parameters of the finite mixture models using data having missing values. Compared with a developed EM algorithm that is proposed earlier the proposed algorithm has proved good performance when the features containing missing values are at least moderately correlated with some of the complete features in the input data set.

Keywords

expectation-maximisation algorithm; neural nets; pattern clustering; regression analysis; EM algorithm; FMM; expectation maximization; finite mixture models; finite mixtures; incremental general regression; missing values; pattern recognition method; Clustering algorithms; Computational modeling; Data models; Educational institutions; Neural networks; Noise; Prediction algorithms; Clustering; Expectation Maximization; Finite Mixtures; Incremental General Regression Neural Network; Missing Values;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Information Technology (WCCIT), 2013 World Congress on

Conference_Location

Sousse

Print_ISBN

978-1-4799-0460-0

Type

conf

DOI

10.1109/WCCIT.2013.6618702

Filename

6618702