# Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data

@article{Tropp2017FixedRankAO, title={Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data}, author={Joel A. Tropp and Alp Yurtsever and Madeleine Udell and Volkan Cevher}, journal={ArXiv}, year={2017}, volume={abs/1706.05736} }

Several important applications, such as streaming PCA and semidefinite programming, involve a large-scale positive-semidefinite (psd) matrix that is presented as a sequence of linear updates. Because of storage limitations, it may only be possible to retain a sketch of the psd matrix. This paper develops a new algorithm for fixed-rank psd approximation from a sketch. The approach combines the Nystrom approximation with a novel mechanism for rank truncation. Theoretical analysis establishes that…

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