Before revision:  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. The proposed scheme reads data samples in a streaming setting with the network minimization problem converging to a low rank approximationwith high precision.
After revision:  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 dimension. The data samples are read in a streaming setting with the network minimization problem converging to a low rank approximationwith high precision.