mixturecode2
所属分类：matlab编程
开发工具：matlab
文件大小：10KB
下载次数：48
上传日期：2011-09-05 14:30:41
上 传 者：
gigitoto
说明： 自适应的选择高斯混合模型个数，并用EM算法估计参数
(Adaptive selection of the number of Gaussian mixture model and estimated parameters using EM algorithm)
文件列表:
uninorm.m (1111, 2010-10-19)
elipsnorm.m (1503, 2010-10-19)
mixtures4.m (19025, 2010-10-19)
demo1.m (328, 2001-08-01)
demo2.m (504, 2001-08-01)
genmix.m (2255, 2010-10-19)
multinorm.m (1375, 2010-10-19)
% -----------------------------------------------------------------------
% Copyright (2002): Mario A. T. Figueiredo and Anil K. Jain
%
% This software is distributed under the terms
% of the GNU General Public License 2.0.
%
% Permission to use, copy, and distribute this software for
% any purpose without fee is hereby granted, provided that this entire
% notice is included in all copies of any software which is or includes
% a copy or modification of this software and in all copies of the
% supporting documentation for such software.
% This software is being provided "as is", without any express or
% implied warranty. In particular, the authors do not make any
% representation or warranty of any kind concerning the merchantability
% of this software or its fitness for any particular purpose."
% ----------------------------------------------------------------------
This is a set of MATLAB m-files implementing the mixture
fitting algorithm described in the paper
M. Figueiredo and A.K.Jain, "Unsupervised learning of
finite mixture models", IEEE Transaction on Pattern Analysis
and Machine Intelligence, vol. 24, no. 3, pp. 381-396, March 2002.
It consists of a main MATLAB function called "mixtures4.m" and
three auxiliary functions: "uninorm.m", "multinorm.m", and
"elipsnorm.m", which are called by the main program.
For instructions type "help mixtures4" at the MATLAB prompt,
or read the first few lines of the "mixtures4.m" file.
Also included are two simple demos which exemplify how to use the
program. The first one, "demo1.m", uses the three component mixture
from the paper
N. Ueda and R. Nakano, "Deterministic annealing EM algorithm",
Neural Networks, vol. 11, pp. 271-282, 19***.
The second one, "demo2.m", uses the "Simulated Set 2" from the
book by McLachlan and Peel, 2000, page 218. These demos call a
function "genmix.m", which generates samples from a Gaussian
mixture; this function is also included.
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