title: PCA memory error in Sklearn: Alternative Dim Reduction?
body: <p>I am trying to reduce the dimensionality of a very large matrix using PCA in Sklearn, but it produces a memory error (RAM required exceeds 128GB). I have already set copy=False and I'm using the less computationally expensive randomised PCA. </p>
  
  <p>Is there a workaround? If not, what other dim reduction techniques could I use that require less memory. Thank you.</p>
  
  <hr>
  
  <p>Update: the matrix I am trying to PCA is a set of feature vectors. It comes from passing a set of training images through a pretrained CNN. The matrix is [300000, 51200]. PCA components tried: 100 to 500.</p>
  
  <p>I want to reduce its dimensionality so I can use these features to train an ML algo, such as XGBoost. Thank you.</p>