Understanding PCA for Dimensionality Reduction

Learn the basics of Principal Component Analysis (PCA).

  1. Standardize data:
def standardize(data):
    mean = sum(data) / len(data)
    std = (sum((x-mean)**2 for x in data) / len(data)) ** 0.5
    return [(x-mean)/std for x in data]
  1. Calculate covariance matrix:
def covariance_matrix(data):
    n = len(data[0])
    return [[sum(data[i][k]*data[j][k] for k in range(n)) for j in range(n)] for i in range(n)]

Read more: PCA Algorithm