Faq how to automate PCA analysis for multiple images
Issue:
How do I automate PCA analysis for multiple images?
Possible Solutions:
Because the version of IMGPCA provided in the standard PLS_Toolbox requires some inputs to operate, IMGPCA is not well suited to automated analysis. Although future versions of the PLS_Toolbox will allow this, the current version requires you use the basic PCA routine and handle the image aspects yourself.
First, create a preprocessing structure use the preprocess function:
>> s = preprocess;
This will bring up a dialog box that lets you specify what preprocessing you want. When you click "OK" it will return a preprocessing structure, s. (Alternatively, you can request the preprocessing method directly using the 'default' keyword. See "preprocess help" for more information.)
s = description: 'Mean Center' calibrate: {'[data,out{1}] = mncn(data);'} apply: {'data = scale(data,out{1});'} undo: {'data = rescale(data,out{1});'} out: {} settingsgui: settingsonadd: 0 usesdataset: 0 caloutputs: 1 keyword: 'Mean Center' userdata: []
Next, create a PCA options structure using:
>> opts = pca('options')
opts = name: 'options' display: 'on' plots: 'final' outputversion: 3 preprocessing: {[]} blockdetails: 'standard'
then put the preprocessing structure s into this:
>> opts.preprocessing{1} = s
opts = name: 'options' display: 'on' plots: 'final' outputversion: 3 preprocessing: {[1x1 struct]} blockdetails: 'standard'
Turn off the display, and turn the plots to 'none':
>> opts.display = 'off'; >> opts.plots = 'none';
Take you data and reshape it to number of pixels by number of channels (3 in your case) using the 'reshape' function. >> data = reshape(data,size(data,1)*size(data,2),size(data,3)); Then use the PCA function like:
>> model = pca(data,2,opts);
The loadings will be in the model.loads field.
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