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

被引：0

作者：

Abas, Ahmed R. ^{[1
]}

机构：

[1] Umm Al Qura Univ, Coll Comp Lith, Dept Comp Sci, Makka Al Mukarrama, Saudi Arabia

来源：

WORLD CONGRESS ON COMPUTER & INFORMATION TECHNOLOGY (WCCIT 2013) | 2013年

关键词：

Clustering; Expectation Maximization; Finite Mixtures; Missing Values; Incremental General Regression Neural Network;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页数：6

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