A Distributed and Incremental SVD Algorithm for Agglomerative Data Analysis on Large Networks

This is the authors' abstract. We don't add key points for this paper.

In this paper it is shown that the SVD of a matrix can be constructed efficiently in a hierarchical approach. The proposed algorithm is proven to recover the singular values and left singular vectors of the input matrix A if its rank is known. Further, the hierarchical algorithm can be used to recover the d largest singular values and left singular vectors with bounded error. It is also shown that the proposed method is stable with respect to round-off errors or corruption of the original matrix entries. Numerical experiments validate the proposed algorithms and parallel cost analysis.