SketchySVD

This talk asserts that randomized linear algebra is a natural tool for on-the-fly compression of data matrices that arise from large-scale scientific simulations and data collection. The technical contribution consists in a new algorithm for constructing an accurate low-rank approximation of a huge matrix from streaming data. Among other applications, we show how the SVD of a large-scale sea surface temperature dataset exposes features of the global climate.