Exercise: PCA in 2D

Step 0: Load data

The starter code contains code to load 45 2D data points. When plotted using the scatter function, the results should look like the following:

Step 1: Implement PCA

In this step, you will implement PCA to obtain x_rot, the matrix in which the data is "rotated" to the basis comprising made up of the principal components

Step 1a: Finding the PCA basis

Find and , and draw two lines in your figure to show the resulting basis on top of the given data points.

Step 1b: Check xRot

Compute xRot, and use the scatter function to check that xRot looks as it should, which should be something like the following:

Step 2: Dimension reduce and replot

In the next step, set k, the number of components to retain, to be 1

Step 3: PCA Whitening

Step 4: ZCA Whitening

Code

close all

%%================================================================

%% Step : Load data

%  We have provided the code to load data from pcaData.txt into x.

%  x is a  *  matrix, where the kth column x(:,k) corresponds to

%  the kth data point.Here we provide the code to load natural image data into x.

%  You do not need to change the code below.

x = load('pcaData.txt','-ascii'); % 载入数据

figure();

scatter(x(, :), x(, :)); % 用圆圈绘制出数据分布

title('Raw data');

%%================================================================

%% Step 1a: Implement PCA to obtain U

%  Implement PCA to obtain the rotation matrix U, which is the eigenbasis

%  sigma. 

% -------------------- YOUR CODE HERE --------------------

u = zeros(size(x, )); % You need to compute this

[n m]=size(x);

% x=x-repmat(mean(x,),,m);  %预处理，均值为零 —— 2维，每一维减去该维上的均值

sigma=(1.0/m)*x*x'; % 协方差矩阵

[u s v]=svd(sigma);

% --------------------------------------------------------

hold on

plot([ u(,)], [ u(,)]); % 画第一条线

plot([ u(,)], [ u(,)]); % 画第二条线

scatter(x(, :), x(, :));

hold off

%%================================================================

%% Step 1b: Compute xRot, the projection on to the eigenbasis

%  Now, compute xRot by projecting the data on to the basis defined

%  by U. Visualize the points by performing a scatter plot.

% -------------------- YOUR CODE HERE --------------------

xRot = zeros(size(x)); % You need to compute this

xRot=u'*x;

% -------------------------------------------------------- 

% Visualise the covariance matrix. You should see a line across the

% diagonal against a blue background.

figure();

scatter(xRot(, :), xRot(, :));

title('xRot');

%%================================================================

%% Step : Reduce the number of dimensions from  to .

%  Compute xRot again (this time projecting to  dimension).

%  Then, compute xHat by projecting the xRot back onto the original axes

%  to see the effect of dimension reduction

% -------------------- YOUR CODE HERE --------------------

k = ; % Use k =  and project the data onto the first eigenbasis

xHat = zeros(size(x)); % You need to compute this

xHat = u*([u(:,),zeros(n,)]'*x); % 降维

% 使特征点落在特征向量所指的方向上而不是原坐标系上

% --------------------------------------------------------

figure();

scatter(xHat(, :), xHat(, :));

title('xHat');

%%================================================================

%% Step : PCA Whitening

%  Complute xPCAWhite and plot the results.

epsilon = 1e-;

% -------------------- YOUR CODE HERE --------------------

xPCAWhite = zeros(size(x)); % You need to compute this

xPCAWhite = diag(./sqrt(diag(s)+epsilon))*u'*x;  % 每个特征除以对应的特征向量，以使每个特征有一致的方差

% --------------------------------------------------------

figure();

scatter(xPCAWhite(, :), xPCAWhite(, :));

title('xPCAWhite');

%%================================================================

%% Step : ZCA Whitening

%  Complute xZCAWhite and plot the results.

% -------------------- YOUR CODE HERE --------------------

xZCAWhite = zeros(size(x)); % You need to compute this

xZCAWhite = u*diag(./sqrt(diag(s)+epsilon))*u'*x;

% --------------------------------------------------------

figure();

scatter(xZCAWhite(, :), xZCAWhite(, :));

title('xZCAWhite');

%% Congratulations! When you have reached this point, you are done!

%  You can now move onto the next PCA exercise. :)

巴特西

Exercise: PCA in 2D

Step 0: Load data

Step 1: Implement PCA

Step 1a: Finding the PCA basis

Step 1b: Check xRot

Step 2: Dimension reduce and replot

Step 3: PCA Whitening

Step 4: ZCA Whitening

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