Range-Net: A High Precision Neural SVD

For Big Data applications, computing a rank-$r$ Singular Value Decomposition (SVD) is restrictive due to the main memory requirements. Recently introduced streaming Randomized SVD schemes work under the restrictive assumption that the singular value spectrum of the data has an exponential decay. This is seldom true for any practical data. Further, the approximation errors in the singular vectors and values are high due to the randomized projection. We present Range-Net as a low memory alternative to rank-$r$ SVD that satisfies the lower bound on tail-energy given by Eckart-Young-Mirsky (EYM) theorem at machine precision. Range-Net is a deterministic two-stage neural optimization approach with random initialization, 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 manner with the network minimization problem converging to the desired rank-$r$ approximation. Range-Net is fully interpretable where all the network outputs and weights have a specific meaning. We provide theoretical guarantees that Range-Net extracted SVD factors satisfy EYM tail-energy lower bound with numerical experiments on real datasets at various scales that confirm these bounds. A comparison against the state-of-the-art streaming Randomized SVD shows that Range-Net is six orders of magnitude more accurate in terms of tail energy while correctly extracting the singular values and vectors.