How to Whiten my data before plugging it into PCA
I am applying PCA to some ECG features, that I extracted. I found some code and learned it from the Matlab website, to apply PCA. However, I would like to whiten my data before applying PCA to it. My data is a matrix (125 x 9), with 9 features, and 125 tests. My matrix name is ratings.
I have two blocks of code: PCA code and whitening code. My PCA code doesn't include whitening, but I hope by answering this question, it will.
Whitening Code
I subtracted the mean of each column from the matrix, as follows:
mean_ratings=mean(ratings); %get mean of each column
ratings=ratings-repmat(mean_ratings, 375,1); %subtract mean of each
%column from each element of the column
I am a little but stuck on what to do after this. I calculated my , by following the sample code:
sigma = ratings * ratings' / size(ratings, 2); %calculate the value of sigma
[U,S,V] = svd(sigma); %get S value and eigen vectors
xPCAwhite = diag(1./sqrt(diag(S) + epsilon)) * U' * ratings; %calculate
%whitened data
PCA Code
I perform my PCA, without considering whitening, by using the code below:
C = corr(ratings,ratings);
w=1./var(ratings);
[wcoeff,score,latent,tsquared,explained] = pca(ratings,...
'VariableWeights',w);
coefforth = inv(diag(std(ratings)))*wcoeff;
cscores = zscore(ratings)*coefforth;
figure()
plot(score(:,1),score(:,2),'+')
xlabel('1st Principal Component')
ylabel('2nd Principal Component')
Now my question is that, do I just plug in the variable 'xPCAwhite' instead of ratings for my PCA code?
matlab pca
add a comment |
I am applying PCA to some ECG features, that I extracted. I found some code and learned it from the Matlab website, to apply PCA. However, I would like to whiten my data before applying PCA to it. My data is a matrix (125 x 9), with 9 features, and 125 tests. My matrix name is ratings.
I have two blocks of code: PCA code and whitening code. My PCA code doesn't include whitening, but I hope by answering this question, it will.
Whitening Code
I subtracted the mean of each column from the matrix, as follows:
mean_ratings=mean(ratings); %get mean of each column
ratings=ratings-repmat(mean_ratings, 375,1); %subtract mean of each
%column from each element of the column
I am a little but stuck on what to do after this. I calculated my , by following the sample code:
sigma = ratings * ratings' / size(ratings, 2); %calculate the value of sigma
[U,S,V] = svd(sigma); %get S value and eigen vectors
xPCAwhite = diag(1./sqrt(diag(S) + epsilon)) * U' * ratings; %calculate
%whitened data
PCA Code
I perform my PCA, without considering whitening, by using the code below:
C = corr(ratings,ratings);
w=1./var(ratings);
[wcoeff,score,latent,tsquared,explained] = pca(ratings,...
'VariableWeights',w);
coefforth = inv(diag(std(ratings)))*wcoeff;
cscores = zscore(ratings)*coefforth;
figure()
plot(score(:,1),score(:,2),'+')
xlabel('1st Principal Component')
ylabel('2nd Principal Component')
Now my question is that, do I just plug in the variable 'xPCAwhite' instead of ratings for my PCA code?
matlab pca
add a comment |
I am applying PCA to some ECG features, that I extracted. I found some code and learned it from the Matlab website, to apply PCA. However, I would like to whiten my data before applying PCA to it. My data is a matrix (125 x 9), with 9 features, and 125 tests. My matrix name is ratings.
I have two blocks of code: PCA code and whitening code. My PCA code doesn't include whitening, but I hope by answering this question, it will.
Whitening Code
I subtracted the mean of each column from the matrix, as follows:
mean_ratings=mean(ratings); %get mean of each column
ratings=ratings-repmat(mean_ratings, 375,1); %subtract mean of each
%column from each element of the column
I am a little but stuck on what to do after this. I calculated my , by following the sample code:
sigma = ratings * ratings' / size(ratings, 2); %calculate the value of sigma
[U,S,V] = svd(sigma); %get S value and eigen vectors
xPCAwhite = diag(1./sqrt(diag(S) + epsilon)) * U' * ratings; %calculate
%whitened data
PCA Code
I perform my PCA, without considering whitening, by using the code below:
C = corr(ratings,ratings);
w=1./var(ratings);
[wcoeff,score,latent,tsquared,explained] = pca(ratings,...
'VariableWeights',w);
coefforth = inv(diag(std(ratings)))*wcoeff;
cscores = zscore(ratings)*coefforth;
figure()
plot(score(:,1),score(:,2),'+')
xlabel('1st Principal Component')
ylabel('2nd Principal Component')
Now my question is that, do I just plug in the variable 'xPCAwhite' instead of ratings for my PCA code?
matlab pca
I am applying PCA to some ECG features, that I extracted. I found some code and learned it from the Matlab website, to apply PCA. However, I would like to whiten my data before applying PCA to it. My data is a matrix (125 x 9), with 9 features, and 125 tests. My matrix name is ratings.
I have two blocks of code: PCA code and whitening code. My PCA code doesn't include whitening, but I hope by answering this question, it will.
Whitening Code
I subtracted the mean of each column from the matrix, as follows:
mean_ratings=mean(ratings); %get mean of each column
ratings=ratings-repmat(mean_ratings, 375,1); %subtract mean of each
%column from each element of the column
I am a little but stuck on what to do after this. I calculated my , by following the sample code:
sigma = ratings * ratings' / size(ratings, 2); %calculate the value of sigma
[U,S,V] = svd(sigma); %get S value and eigen vectors
xPCAwhite = diag(1./sqrt(diag(S) + epsilon)) * U' * ratings; %calculate
%whitened data
PCA Code
I perform my PCA, without considering whitening, by using the code below:
C = corr(ratings,ratings);
w=1./var(ratings);
[wcoeff,score,latent,tsquared,explained] = pca(ratings,...
'VariableWeights',w);
coefforth = inv(diag(std(ratings)))*wcoeff;
cscores = zscore(ratings)*coefforth;
figure()
plot(score(:,1),score(:,2),'+')
xlabel('1st Principal Component')
ylabel('2nd Principal Component')
Now my question is that, do I just plug in the variable 'xPCAwhite' instead of ratings for my PCA code?
matlab pca
matlab pca
asked Nov 26 '18 at 3:23
MuhammadMuhammad
174418
174418
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