EM算法及其应用（代码）

最近上模式识别的课需要做EM算法的作业，看了机器学习公开课及网上的一些例子，总结如下：（中间部分公式比较多，不能直接粘贴上去，为了方便用了截图，请见谅）

概要

适用问题

EM算法是一种迭代算法，主要用于计算后验分布的众数或极大似然估计，广泛地应用于缺损数据、截尾数据、成群数据、带有讨厌参数的数据等所谓不完全数据的统计推断问题。

优缺点

优点：EM算法简单且稳定，迭代能保证观察数据对数后验似然是单调不减的。

缺点：对于大规模数据和多维高斯分布，其总的迭代过程，计算量大，迭代速度易受影响；EM算法的收敛速度，非常依赖初始值的设置，设置不当，计算时的代价是相当大的；EM算法中的M-Step依然是采用求导函数的方法,所以它找到的是极值点,即局部最优解,而不一定是全局最优解。

原理

Jensen不等式

与ML估计的关系

EM算法的E-step是建立的下界，M-step是极大化下界，不断重复这两步直到收敛。最大似然估计是求参数的一种相当普遍的方法，但是它的使用场合是除了参数未知外，其它一切都已知，即不存在隐变量的情况。当存在隐变量时，我们可以通过EM算法，消掉隐变量，当期望取极大值时，最大似然函数也会在相同点取极大值。

具体模型应用

问题映射

       用两个高斯函数生成数据，这两个高斯密度函数分别为N(1,2)和N(20,3.5)，分别生成160和240个数据，也就是说其在混合模型中的权重分别为0.4 和 0.6。将这些数据放入同一数组中，经过EM算法可得出混合模型的密度函数及其权重。

代码

clc;
clear;
%generate data fromnormal distribution.
mu1= 1;
sigma1= 2;
R1 = normrnd(mu1,sigma1,160,1);
mu2 = 20;
sigma2 = 3.5;
R2 = normrnd(mu2,sigma2,240,1);
%merge
R = [R1;R2];
%shuffel
r = randperm(size(R,1));
R=R(r,:);
figure,plot(R,'ro');
%用两个高斯函数生成数据，这两个高斯密度函数分别为N(0,2)和N(20,3.5)，分别生成160和240个数据，也就是说其在混合模型中的权重分别为0.4 和 0.6.
[mu,sigma,phi] = mixGuassAnalysis(R,2,15);

clc;
clear;
%generate data fromnormal distribution.
mu1= 1;
sigma1= 2;
R1 = normrnd(mu1,sigma1,160,1);
mu2 = 20;
sigma2 = 3.5;
R2 = normrnd(mu2,sigma2,240,1);
%merge
R = [R1;R2];
%shuffel
r = randperm(size(R,1));
R=R(r,:);
figure,plot(R,'ro');
%用两个高斯函数生成数据，这两个高斯密度函数分别为N(0,2)和N(20,3.5)，分别生成160和240个数据，也就是说其在混合模型中的权重分别为0.4 和 0.6.
[mu,sigma,phi] = mixGuassAnalysis(R,2,15);

function   [mu,sigma,phi]  =mixGuassAnalysis(sampleMatrix,k,maxIteration,epsilon)
%UNTITLED Analysis the kguass distribution by the input matrix m
%sampleMatrix   the matrix of sample, in which each rowrepresents a sample.
%k  the number of guass distriubtion
%maxIteration   the max times of iteration,default 100.
%epsilon    the epsilon of loglikelihood,default 0.00001.
%check parameters
if nargin < 4
    epsilon = 0.00001;
    if nargin < 3
        maxIteration = 100;
    end
end
if k==1
    mu = mean(sampleMatrix);
    sigma = var(sampleMatrix);
    phi = 1;
    return;
end
[sampleNum,dimensionality] =size(sampleMatrix);
%init k guassdistribution
mu = zeros(k,dimensionality);
for i=1:1:dimensionality
    colVector = sampleMatrix(:,i);
    maxV = max(colVector);
    minV= min(colVector);
    mu(1,i) = minV;
    mu(k,i) = maxV;
    for j=2:1:k-1
        mu(j:i) =  minv+(j-1)*(maxV-minV)/(k-1);
    end
end
sigma =zeros(k,dimensionality,dimensionality);
for i=1:1:k
    d = rand();
    sigma(i,:) = 10*d*eye(dimensionality);
end
phi = zeros(1,k);
for i=1:1:k
    phi(1,i) = 1.0/k;
end
%the weight of sample iis generated by guass distribution j
weight = zeros(sampleNum,k);
oldlikelihood = -inf;
for iter=1:maxIteration
    loglikelihood = 0;
    %E-step
    for i=1:1:sampleNum
        for j = 1:1:k
           weight(i,j)=mvnpdf(sampleMatrix(i,:),mu(j,:),reshape(sigma(j,:),dimensionality,dimensionality))*phi(j);
        end

        sum = 0;
        for j = 1:1:k
            sum = sum+weight(i,j);
        end

        loglikelihood = loglikelihood +log(sum);

        for j = 1:1:k
            weight(i,j)=weight(i,j)/sum;
        end
    end

    if abs(loglikelihood-oldlikelihood)
        break;
    else
        oldlikelihood = loglikelihood;
    end
    %M-step

    %updatephi
    for i=1:1:k
        sum = 0;
        for j=1:1:sampleNum
            sum = sum+weight(j,i);
        end
        phi(i) = sum/sampleNum;
    end

    %updatemu
    for i=1:1:k
        sum = zeros(1,dimensionality);
        for j=1:1:sampleNum
            sum =  sum+weight(j,i)*sampleMatrix(j,:);
        end

        mu(i,:) =  sum/(phi(i)*sampleNum);
    end
    mu1(iter) = mu(1);
    mu2(iter) = mu(2);
    %updatesigma
    for i=1:1:k
        sum =zeros(dimensionality,dimensionality);
        for j=1:1:sampleNum
            sum = sum+ weight(j,i)*(sampleMatrix(j,:)-mu(i,:))'*(sampleMatrix(j,:)-mu(i,:));
        end
        sigma(i,:) = sum/(phi(i)*sampleNum);
    end

end
sigma = sqrt(sigma);
end

function   [mu,sigma,phi]  =mixGuassAnalysis(sampleMatrix,k,maxIteration,epsilon)
%UNTITLED Analysis the kguass distribution by the input matrix m
%sampleMatrix   the matrix of sample, in which each rowrepresents a sample.
%k  the number of guass distriubtion
%maxIteration   the max times of iteration,default 100.
%epsilon    the epsilon of loglikelihood,default 0.00001.
%check parameters
if nargin < 4
    epsilon = 0.00001;
    if nargin < 3
        maxIteration = 100;
    end
end
if k==1
    mu = mean(sampleMatrix);
    sigma = var(sampleMatrix);
    phi = 1;
    return;
end
[sampleNum,dimensionality] =size(sampleMatrix);
%init k guassdistribution
mu = zeros(k,dimensionality);
for i=1:1:dimensionality
    colVector = sampleMatrix(:,i);
    maxV = max(colVector);
    minV= min(colVector);
    mu(1,i) = minV;
    mu(k,i) = maxV;
    for j=2:1:k-1
        mu(j:i) =  minv+(j-1)*(maxV-minV)/(k-1);
    end
end
sigma =zeros(k,dimensionality,dimensionality);
for i=1:1:k
    d = rand();
    sigma(i,:) = 10*d*eye(dimensionality);
end
phi = zeros(1,k);
for i=1:1:k
    phi(1,i) = 1.0/k;
end
%the weight of sample iis generated by guass distribution j
weight = zeros(sampleNum,k);
oldlikelihood = -inf;
for iter=1:maxIteration
    loglikelihood = 0;
    %E-step
    for i=1:1:sampleNum
        for j = 1:1:k
           weight(i,j)=mvnpdf(sampleMatrix(i,:),mu(j,:),reshape(sigma(j,:),dimensionality,dimensionality))*phi(j);
        end
       
        sum = 0;
        for j = 1:1:k
            sum = sum+weight(i,j);
        end
       
        loglikelihood = loglikelihood +log(sum);
    
        for j = 1:1:k
            weight(i,j)=weight(i,j)/sum;
        end
    end
    
    if abs(loglikelihood-oldlikelihood)




迭代轨迹

问题

       本例用到的是伪随机组成的数据，本身具有一定的模型，最近结果可看出迭代次数只需要6次就达到下界。实际中将会有更多噪声及隐藏变量。

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