Before revision:  This latter consideration can still be memory intensive when the data dimension is large or extraneous when the desired rank approximation is close to the full rank. We present a two stage neural optimization approach as an alternative to conventional and randomized SVD techniques , where the  memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample size.
After revision:  This latter consideration can still be memory intensive or worse can exceed the feature dimension of the data. To address these issues, we present Range-Net as an alternative to conventional and randomized SVD techniques , where the memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample size.