173 search results - page 1 / 35 » Dynamical low-rank approximation: applications and numerical... |
SDM
2011
SIAM
12 years 7 months ago
2011
SIAM
In this paper we present a fast and accurate procedure called clustered low rank matrix approximation for massive graphs. The procedure involves a fast clustering of the graph and...
SIAMSC
2011
12 years 11 months ago
2011
We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inf...
IDEAL
2010
Springer
13 years 1 months ago
2010
Springer
: Covariance matrices capture correlations that are invaluable in modeling real-life datasets. Using all d2 elements of the covariance (in d dimensions) is costly and could result ...
TSP
2008
13 years 4 months ago
2008
Abstract--Low-rank matrix approximation has applications in many fields, such as 2D filter design and 3D reconstruction from an image sequence. In this paper, one issue with low-ra...
MCS
2008
Springer
13 years 4 months ago
2008
Springer
Dynamical low-rank approximation is a differential-equation based approach to efficiently computing low-rank approximations to time-dependent large data matrices or to solutions o...