UFLDL教程之（三）PCA and Whitening exercise

系统管理员 2021-07-16 17:30 301阅读 0赞

[Exercise:PCA and Whitening][Exercise_PCA and Whitening]

**第0步：数据准备**

UFLDL下载的文件中，包含数据集IMAGES\_RAW，它是一个512\*512\*10的矩阵，也就是10幅512\*512的图像

**（a）载入数据**

利用sampleIMAGESRAW函数，从IMAGES\_RAW中提取numPatches个图像块儿，每个图像块儿大小为patchSize，并将提取到的图像块儿按列存放，分别存放在在矩阵patches的每一列中，即patches(:,i)存放的是第i个图像块儿的所有像素值

**（b）数据去均值化处理**

将每一个图像块儿的所有像素值都减去该图像块儿的平均像素值，实现数据的去均值化

下图是显示的随机选取的图像块儿

![271503287651852.jpg][]

**第一步：执行PCA**

该部分分为两部分

**（1）进行PCA计算，这里仅仅对数据x进行旋转得到xrot，而不进行主成分的提取**

具体地：

①计算数据x的协方差矩阵sigma

![271519180789570.png][]

②对sigma进行特征分解，利用matlab的eig函数，从而得到sigma的特征向量构成的矩阵U

\[U,S,V\]=eig(sigma);

U=\[u1,...,ui,...,un\]，它的每一列分别是sigma的特征向量，n是输入数据的特征维数

S=diag(\[λ1,...λi,...,λn\])是由sigma的特征值作为对角元素的对角阵，ui和λi相对应；

为了后续的计算，这里要将U的各列次序进行调换，使得调换后的各列所对应的特征值大小依次递减;

调换后的矩阵仍记作U，相应的特征值对角阵仍即为S，即：

U=\[u1,...,ui,...,un\]，S=diag(\[λ1,...λi,...,λn\])，满足：λ1>=...>=λi>=...>=λn

③利用矩阵U对数据x进行旋转，得到xrot，即xrot=U‘\*x

**（2）对旋转后的数据求解协方差矩阵covar，并将其可视化，观察得到的选择后的数据是否正确**

PCA保证选择后的数据的协方差矩阵是一个对角阵，如果covar是正确的

那么它的图像应该是一个蓝色背景，并且在对角线位置有一斜线

这里显示协方差矩阵covar利用了matlab的imagesc，该函数真的很强大呀

imagesc(covar)的作用是：把矩阵covar以图像形式显示出来，矩阵中不同的数值会被赋予不同的颜色

得到的协方差矩阵的图像如下：可以看到，图像处了对角线位置外，其余部分颜色都相同

![271537486569025.jpg][]

**第二步：满足条件的主成分个数**

本部分，找到满足条件的主成分的个数k

也就是找到最小的k值，使得(λ1+...+ λk)/(λ1+...+ λn)>某个百分数，如99%

**第三步：利用找到的主成分个数，对数据进行降维**

在第二步，已经找到了数字k，也就是，保留数据的k个主成分就满足了要求

在该步，将对数据x进行降维，只留下k个主成分，得到xTidle

同时，为了观察降维后的数据的好坏，在利用U(:,k)将降维后的数据变换会原来的维数，也就是得到了原数据的近似恢复数据

并利用网格将恢复出的图像显示出，与原图像进行比较，下面第一幅图是由降维后的图像恢复出的原数据，下图是相应的原数据，可以发现，降维后的数据基本可以恢复出于原数据非常相近的数据

![271552062971408.jpg][]![271552158445057.jpg][]

**第四步：PCA白化+正则化**

该部分分为两步

**（1）执行具有白化和正则化的PCA**

首先，对数据进行旋转（利用特征矩阵U）

然后，利用特征值对旋转后的数据进行缩放，实现白化

同时，在利用特征值缩放时，利用参数ε对特征值进行微调，实现正则化

![271631124694609.png][]

**（b）计算百化后的数据的协方差矩阵，观察该协方差矩阵**

如果加入了正则化项，则该协方差矩阵的对角线元素都小于1

如果没有加入正则项（即仅有旋转+白化），则该协方差矩阵的对角线元素都为1（实际上，是令ε为一个极小的数）

下图是白化后数据的协方差矩阵对应的图像，上图是加入正则化后的结果，下图是没有加入正则化后的结果

![271656099842208.jpg][]

![271657432976249.jpg][]

**第五步：ZCA白化**

ZCA白化，就是在PCA白化的基础上做了一个旋转，即

![271923492655269.png][]

下面的第一幅图是ZCA白化后的结果图，第二幅图是相应的原始图像

可以看到，ZCA白化的结果图似乎是原始图像的边缘

![271926024063210.jpg][]![271926112978960.jpg][]

下面，是该部分的pca\_gen的代码

clc
    clear
    close all
    
    %%================================================================
    %% Step 0a: Load data
    %  Here we provide the code to load natural image data into x.
    %  x will be a 144 * 10000 matrix, where the kth column x(:, k) corresponds to
    %  the raw image data from the kth 12x12 image patch sampled.
    %  You do not need to change the code below.
    x = sampleIMAGESRAW();%从IMAGES_RAW中读取一些图像patches
    figure('name','Raw images');%显示一个figure，标题为raw images
    randsel = randi(size(x,2),200,1); % A random selection of samples for visualization
    display_network(x(:,randsel));%显示随机选取的图像块儿
    %% Step 0b: Zero-mean the data (by row)
    %  You can make use of the mean and repmat/bsxfun functions.
    % -------------------- YOUR CODE HERE -------------------- 
    x=x-repmat(mean(x),size(x,1),1);%x的每一列的所有元素都减去该列的均值
    
    
    %%================================================================
    %% Step 1a: Implement PCA to obtain xRot
    %  Implement PCA to obtain xRot, the matrix in which the data is expressed
    %  with respect to the eigenbasis of sigma, which is the matrix U.
    % -------------------- YOUR CODE HERE -------------------- 
    xRot = zeros(size(x)); % You need to compute this
    % 计算协方差矩阵并进行特征值分解
    m=size(x,2);% 输入的样本个数
    sigma=x*x'/m;%输入数据的协方差矩阵
    [U,S,V]=eig(sigma);%对协方差矩阵进行特征值分解
    [S_Value,S_Index]=sort(diag(S),'descend');%提取S的对角线元素，将其按降序排列，sIndex是排序后的编号
    U=U(:,S_Index);
    S=diag(S_Value);
    % 对数据进行旋转
    xRot=U'*x;
    
    %% Step 1b: Check your implementation of PCA
    %  The covariance matrix for the data expressed with respect to the basis U
    %  should be a diagonal matrix with non-zero entries only along the main
    %  diagonal. We will verify this here.
    %  Write code to compute the covariance matrix, covar. 
    %  When visualised as an image, you should see a straight line across the
    %  diagonal (non-zero entries) against a blue background (zero entries).
    % -------------------- YOUR CODE HERE -------------------- 
    covar = zeros(size(x, 1)); % You need to compute this
    covar=xRot*xRot'/m;%旋转数据后的数据对应的协方差矩阵
    % Visualise the covariance matrix. You should see a line across the
    % diagonal against a blue background.
    figure('name','Visualisation of covariance matrix');
    imagesc(covar);
    
    %%================================================================
    %% Step 2: Find k, the number of components to retain
    %  Write code to determine k, the number of components to retain in order
    %  to retain at least 99% of the variance.
    % -------------------- YOUR CODE HERE -------------------- 
    k = 0; % Set k accordingly
    S_diag=diag(S);
    S_sum=sum(S_diag);
    for k=1:size(x,1)
        Sk_sum=sum(S_diag(1:k));
        if Sk_sum/S_sum>=0.99
            break;
        end
    end
    
    %%================================================================
    %% Step 3: Implement PCA with dimension reduction
    %  Now that you have found k, you can reduce the dimension of the data by
    %  discarding the remaining dimensions. In this way, you can represent the
    %  data in k dimensions instead of the original 144, which will save you
    %  computational time when running learning algorithms on the reduced
    %  representation.
    % 
    %  Following the dimension reduction, invert the PCA transformation to produce 
    %  the matrix xHat, the dimension-reduced data with respect to the original basis.
    %  Visualise the data and compare it to the raw data. You will observe that
    %  there is little loss due to throwing away the principal components that
    %  correspond to dimensions with low variation.
    
    % -------------------- YOUR CODE HERE -------------------- 
    % 对数据进行降维
    xTidle=U(:,1:k)'*x;
    % 利用降维后的数据xTidle对数据进行恢复
    xHat = zeros(size(x));  % You need to compute this
    xHat = U*[xTidle;zeros(m-k,size(x,2))];
    
    % Visualise the data, and compare it to the raw data
    % You should observe that the raw and processed data are of comparable quality.
    % For comparison, you may wish to generate a PCA reduced image which
    % retains only 90% of the variance.
    
    figure('name',['PCA processed images ',sprintf('(%d / %d dimensions)', k, size(x, 1)),'']);
    display_network(xHat(:,randsel));
    figure('name','Raw images');
    display_network(x(:,randsel));
    
    %%================================================================
    %% Step 4a: Implement PCA with whitening and regularisation
    %  Implement PCA with whitening and regularisation to produce the matrix
    %  xPCAWhite. 
    epsilon =0.000001;
    % -------------------- YOUR CODE HERE -------------------- 
    xPCAWhite = zeros(size(x));
    xPCAWhite=diag(1./sqrt(S_diag+epsilon))*xRot;
    %% Step 4b: Check your implementation of PCA whitening 
    %  Check your implementation of PCA whitening with and without regularisation. 
    %  PCA whitening without regularisation results a covariance matrix 
    %  that is equal to the identity matrix. PCA whitening with regularisation
    %  results in a covariance matrix with diagonal entries starting close to 
    %  1 and gradually becoming smaller. We will verify these properties here.
    %  Write code to compute the covariance matrix, covar. 
    %
    %  Without regularisation (set epsilon to 0 or close to 0), 
    %  when visualised as an image, you should see a red line across the
    %  diagonal (one entries) against a blue background (zero entries).
    %  With regularisation, you should see a red line that slowly turns
    %  blue across the diagonal, corresponding to the one entries slowly
    %  becoming smaller.
    % -------------------- YOUR CODE HERE -------------------- 
    covar=xPCAWhite*xPCAWhite'/m;
    % Visualise the covariance matrix. You should see a red line across the
    % diagonal against a blue background.
    figure('name','Visualisation of covariance matrix');
    imagesc(covar);
    
    %%================================================================
    %% Step 5: Implement ZCA whitening
    %  Now implement ZCA whitening to produce the matrix xZCAWhite. 
    %  Visualise the data and compare it to the raw data. You should observe
    %  that whitening results in, among other things, enhanced edges.
    xZCAWhite = zeros(size(x));
    % -------------------- YOUR CODE HERE -------------------- 
    xZCAWhite=U*xPCAWhite;%ZCA白化即在PCA白化基础上做了一个旋转
    % Visualise the data, and compare it to the raw data.
    % You should observe that the whitened images have enhanced edges.
    figure('name','ZCA whitened images');
    display_network(xZCAWhite(:,randsel));
    figure('name','Raw images');
    display_network(x(:,randsel));

[Exercise_PCA and Whitening]: http://ufldl.stanford.edu/wiki/index.php/Exercise:PCA_and_Whitening
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[271537486569025.jpg]: /images/20210716/947775bfa2ed48e8af821e8ba0413acf.png
[271552062971408.jpg]: http://images0.cnblogs.com/blog2015/736761/201508/271552062971408.jpg
[271552158445057.jpg]: /images/20210716/c509433eab05458b9ff73c6d4ac829e3.png
[271631124694609.png]: /images/20210716/ac27c07257ea4745b19c84dc94c2534a.png
[271656099842208.jpg]: /images/20210716/5608aec1b8c046daa364bd9ea7f895ce.png
[271657432976249.jpg]: /images/20210716/debebe4f0fb146e6996d642d3dc493dc.png
[271923492655269.png]: /images/20210716/a14878f7063d4812b80c41d78f59ea16.png
[271926024063210.jpg]: http://images0.cnblogs.com/blog2015/736761/201508/271926024063210.jpg
[271926112978960.jpg]: /images/20210716/5dbd65107dc34c7ea6ec3ec7aad7adf7.png