Acceleration of the EM algorithm using the Vector Aitken method and its Steffensen form

2017 ◽  
Vol 33 (1) ◽  
pp. 175-182
Author(s):  
Xu Guo ◽  
Qiu-yue Li ◽  
Wang-li Xu
Keyword(s):  
Em Algorithm ◽  
The Em Algorithm ◽  
Aitken Method

Multi-User Detection Analysis via Aitken-δ2 EM Acceleration Algorithm

2011 ◽  
Vol 225-226 ◽  
pp. 284-288
Author(s):  
Jin Long Xian ◽  
Jian Wu Li
Keyword(s):  
Missing Data ◽  
Em Algorithm ◽  
Iterative Algorithm ◽  
Convergence Speed ◽  
Speed Of Convergence ◽  
Detection Analysis ◽  
The Em Algorithm ◽  
Aitken Method

The EM iterative algorithm is commonly used in recent years for missing data, which has the character of easy and popular applicability. But the EM algorithm has a fatal weakness that the convergence speed is slowly; Acceleration of the EM algorithm using the Aitken method is proposed in order to solve this problem.In Multi-user Detection, via this accelerated algorithm, we get a good performance which trends to ML performance, and compared its speed of convergence with the EM algorithm that Aitken-acceleration algorithm has faster convergence than the standard EM algorithm, and we also illustrate the performance of simulation.

EM Algorithm Based on Fuzzification and its Application

2012 ◽  
Vol 532-533 ◽  
pp. 1445-1449
Author(s):  
Ting Ting Tong ◽  
Zhen Hua Wu
Keyword(s):  
Em Algorithm ◽  
Mixed Model ◽  
Image Data ◽  
Training Sample ◽  
Model Parameters ◽  
Parameter Learning ◽  
Weighted Degree ◽  
The Em Algorithm ◽  
The Impact

EM algorithm is a common method to solve mixed model parameters in statistical classification of remote sensing image. The EM algorithm based on fuzzification is presented in this paper to use a fuzzy set to represent each training sample. Via the weighted degree of membership, different samples will be of different effect during iteration to decrease the impact of noise on parameter learning and to increase the convergence rate of algorithm. The function and accuracy of classification of image data can be completed preferably.

scholarly journals PENDUGAAN DATA HILANG DENGAN METODE YATES DAN ALGORITMA EM PADA RANCANGAN LATTICE SEIMBANG

2015 ◽  
Vol 4 (2) ◽  
pp. 74
Author(s):  
MADE SUSILAWATI ◽  
KARTIKA SARI
Keyword(s):  
Missing Data ◽  
Em Algorithm ◽  
Basic Concept ◽  
Incomplete Data ◽  
Likelihood Function ◽  
Animal Husbandry ◽  
Lattice Design ◽  
The Em Algorithm ◽  
Missing Data Estimation ◽  
Data Estimation

Missing data often occur in agriculture and animal husbandry experiment. The missing data in experimental design makes the information that we get less complete. In this research, the missing data was estimated with Yates method and Expectation Maximization (EM) algorithm. The basic concept of the Yates method is to minimize sum square error (JKG), meanwhile the basic concept of the EM algorithm is to maximize the likelihood function. This research applied Balanced Lattice Design with 9 treatments, 4 replications and 3 group of each repetition. Missing data estimation results showed that the Yates method was better used for two of missing data in the position on a treatment, a column and random, meanwhile the EM algorithm was better used to estimate one of missing data and two of missing data in the position of a group and a replication. The comparison of the result JKG of ANOVA showed that JKG of incomplete data larger than JKG of incomplete data that has been added with estimator of data. This suggest  thatwe need to estimate the missing data.

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