Svd, Data Science, Machine Learning, Data Visualization, Fluid Dynamics.
compresses the original image, necessitating the storage of solely the first 240 columns of U and V matrices, along with the first 240 diagonal elements of the S matrix in U_Hat, S_Hat, and V_Hat. Consequently, SVD serves as a pivotal tool for data compression, preserving the essence of the data