Svd, Matrix Decomposition, Data Processing, Feature Engineering, Unsupervised Learning.
effective dimension (rank) matches the actual dimension (since we are in the singular vector feature space from SVD). import numpy as np import matplotlib.pyplot as plt # generate the data n_samples = 500 C = np.array([[0.1, 0.6], [2., .6]]) X = np.random.randn(n_samples, 2) @ C + np.array([-6, 3])