6 code implementations • 6 Aug 2016 • N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz
The essential idea of probabilistic algorithms is to employ some amount of randomness in order to derive a smaller matrix from a high-dimensional data matrix.
Computation Mathematical Software Methodology
1 code implementation • 24 Mar 2015 • Per-Gunnar Martinsson, Sergey Voronin
The method takes as input a tolerance $\varepsilon$ and an $m\times n$ matrix $A$, and returns an approximate low rank factorization of $A$ that is accurate to within precision $\varepsilon$ in the Frobenius norm (or some other easily computed norm).
Numerical Analysis
5 code implementations • 18 Feb 2015 • Sergey Voronin, Per-Gunnar Martinsson
The ID and CUR factorizations pick subsets of the rows/columns of a matrix to use as bases for its row/column space.
Numerical Analysis Mathematical Software
1 code implementation • 29 Dec 2014 • Sergey Voronin, Per-Gunnar Martinsson
The manuscript describes efficient algorithms for the computation of the CUR and ID decompositions.
Numerical Analysis Numerical Analysis
