Svd, Data Science, Machine Learning, Data Visualization, Fluid Dynamics.
80, 120, 240 ### Approximation mat_approx = U[:, :k] @ Sigma[:k, :k] @ VT[:k, :] With a rank (r) of 240, the reconstructed image appears notably accurate, with the singular values encompassing nearly 55% of the image variance. This truncation via Singular Value Decomposition (SVD) effectively