Iterative SVD (Iterative Singular Value Decomposition) is a robust algorithm for imputing missing values in data matrices. It employs an EM-like two-step process: estimating missing entries (E-step) and updating the low-rank approximation via SVD (M-step). These steps iteratively refine the matrix, preserving its underlying multivariate structure. Ideal for multivariate datasets with missing data.
