Algorithm of Principal Component Analysis (PCA)

algorithm
machine-learning
principal-component-analysis

(Team) #1

Principal component analysis (PCA) is a technique to bring out strong patterns in a dataset by supressing variations. It is used to clean data sets to make it easy to explore and analyse. The steps involved are:

  • Step 1: Get your data
  • Step 2: Give your data a structure
  • Step 3: Standardize your data
  • Step 4: Get Covariance of Z
  • Step 5: Calculate Eigen Vectors and Eigen Values
  • Step 6: Sort the Eigen Vectors
  • Step 7: Calculate the new features
  • Step 8: Drop unimportant features from the new set


Read the article for the detailed algorithm
This is a companion discussion topic for the original entry at http://iq.opengenus.org/algorithm-principal-component-analysis-pca/