Description: This introduction to the expectation–maximization (EM) algorithm
provides an intuitive and mathematically rigorous understanding of
EM. Two of the most popular applications of EM are described in
detail: estimating Gaussian mixture models (GMMs), and estimat-
ing hidden Markov models (HMMs). EM solutions are also derived
for learning an optimal mixture of fi xed models, for estimating the
parameters of a compound Dirichlet distribution, and for dis-entangling
superimposed signals. Practical issues that arise in the use of EM are
discussed, as well as variants of the algorithm that help deal with these
challenges.,This introduction to the expectation–maximization (EM) algorithm
provides an intuitive and mathematically rigorous understanding of
EM. Two of the most popular applications of EM are described in
detail: estimating Gaussian mixture models (GMMs), and estimat-
ing hidden Markov models (HMMs). EM solutions are also derived
for learning an optimal mixture of fi xed models, for estimating the
parameters of a compound Dirichlet distribution, and for dis-entangling
superimposed signals. Practical issues that arise in the use of EM are
discussed, as well as variants of the algorithm that help deal with these
challenges. Platform: |
Size: 892928 |
Author:steve |
Hits:
Description: 不错的GM_EM代码。用于聚类分析等方面。- GM_EM- fit a Gaussian mixture model to N points located in n-dimensional
space.
Note: This function requires the Statistical Toolbox and, if you wish to
plot (for k = 2), the function error_ellipse
Elementary usage:
GM_EM(X,k)- fit a GMM to X, where X is N x n and k is the number of
clusters. Algorithm follows steps outlined in Bishop
(2009) Pattern Recognition and Machine Learning , Chapter 9.
Additional inputs:
bn_noise- allow for uniform background noise term ( T or F ,
default T ). If T , relevant classification uses the
(k+1)th cluster
reps- number of repetitions with different initial conditions
(default = 10). Note: only the best fit (in a likelihood sense) is
returned.
max_iters- maximum iteration number for EM algorithm (default = 100)
tol- tolerance value (default = 0.01)
Outputs
idx- classification/labelling of data in X
mu- GM centres Platform: |
Size: 3072 |
Author:朱魏 |
Hits:
Description: 基于EM算法实现的高斯混合模型数据分类，可以很优秀的对各种数据进行聚类分析，R语言实现-EM algorithm based on Gaussian mixture model data classification, can be very good for a variety of data clustering analysis, R language Platform: |
Size: 1024 |
Author:李亮民 |
Hits:
Search - EM algorithm for mixture of Gaussian