Module 6: Dimensionality Reduction
  Lecture 30: Principal Component Analysis (PCA)
 

                                            

 

 

Algorithm
  • Mean center the data (optional)
  • Compute the covariance matrix of the dimensions
  • Find eigenvectors of covariance matrix
  • Sort eigenvectors in decreasing order of eigenvalues
  • Project onto eigenvectors in order
  • Assume data matrix is of size
  • For each dimension, compute mean
  • Mean center by subtracting from each column to get
  • Compute covariance matrix of size
 
  • If mean centered,
  • Find eigenvectors and corresponding eigenvalues of
  • Sort eigenvalues such that
  • Project step-by-step onto the principal components , etc.