Please help me to understand the PCA usage.

I want to use HOG for feature extraction and want to apply PCA for dimension reduction ,I found this code but did not understand the PCA function usage. Please make me understand how PCA function is used and what results its providing or Is it used in the code in correct way, If not please help me to make it correct.

        % Features Extraction of Face based on HOG
  clear;
  training=imageSet('Dataset\','recursive'); %  folder name of the database 
  K=1;
  for i=1:size(training,2 )
      for j=1:training(i).Count
          Face=read(training(i),j);
         Face=imresize(Face, [48 48]);
          %Face=rgb2gray(Face); %If color images
           Face = pca(double(Face));
           HOG_Features= extractHOGFeatures(Face);
           trainingFeatures(K,:)=single([HOG_Features]);
           traininglabel{K}=training(i).Description;
          K=K+1;
      end
      persons{i}=training(i).Description;
  end
  traininglabel=traininglabel';
  csvwrite('Test.csv', trainingFeatures)

[Adam]

Have you tried

doc pca

?

[saeeda saher]

Thank You for reply .. will you make me understand according to code, PCA is used for dimension reduction. and i have read many research papers in those papers PCA is used after HOG features. I mean HOG features are passed to the PCA. But here PCA is used before HOG. That is confusing me

[Adam]

I have never worked with HOG features, but if the PCA is being done before the HOG then in this case it is because whoever wrote the code wishes to reduce the dimensionality of their original data before extracting features from the resultant dimension-reduced data. This will remove redundancy that may be present in the original data and can cause feature extraction to either be inefficient or simply not work as well.