Svd, Matrix Decomposition, Data Processing, Feature Engineering, Unsupervised Learning.
X = X - np.mean(X, axis=0) # SVD u,s,vt=np.linalg.svd(X, full_matrices=False) # low rank approximation scopy = s.copy() scopy[1:] = 0.0 low_rank_approx_X = u.dot(np.diag(scopy)).dot(vt) # dimension reduction shifted_X = u.dot(np.diag(s)) dim_reduction_X = u.dot(np.diag(scopy)) Closing Note Let’s