title: scikit-learn PCA for image dataset
body: <p>I am trying to perform PCA on an image dataset with 100.000 images each of size 224x224x3. </p>
  
  <p>I was hoping to project the images into a space of dimension 1000 (or somewhere around that).</p>
  
  <p>I am doing this on my laptop (16gb ram, i7, no GPU) and already set <em>svd_solver='randomized'</em>.</p>
  
  <p>However, fitting takes forever. Is the dataset and the image dimension just too large or is there some trick I could be using?</p>
  
  <p>Thanks!</p>
  
  <p>Edit:</p>
  
  <p>This is the code:</p>
  
  <pre><code>pca = PCA(n_components=1000, svd_solver='randomized')
  pca.fit(X)
  Z = pca.transform(X)
  </code></pre>
  
  <p>X is a 100000 x 150528 matrix whose rows represent a flattened image.</p>