Accelerating jackknife resampling for the Canonical Polyadic Decomposition
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Accelerating jackknife resampling for the Canonical Polyadic Decomposition. / Psarras, Christos; Karlsson, Lars; Bro, Rasmus; Bientinesi, Paolo.
In: Frontiers in Applied Mathematics and Statistics, Vol. 8, 830270, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Accelerating jackknife resampling for the Canonical Polyadic Decomposition
AU - Psarras, Christos
AU - Karlsson, Lars
AU - Bro, Rasmus
AU - Bientinesi, Paolo
PY - 2022
Y1 - 2022
N2 - The Canonical Polyadic (CP) tensor decomposition is frequently used as a model in applications in a variety of different fields. Using jackknife resampling to estimate parameter uncertainties is often desirable but results in an increase of the already high computational cost. Upon observation that the resampled tensors, though different, are nearly identical, we show that it is possible to extend the recently proposed Concurrent ALS (CALS) technique to a jackknife resampling scenario. This extension gives access to the computational efficiency advantage of CALS for the price of a modest increase (typically a few percent) in the number of floating point operations. Numerical experiments on both synthetic and real-world datasets demonstrate that the new workflow based on a CALS extension can be several times faster than a straightforward workflow where the jackknife submodels are processed individually.
AB - The Canonical Polyadic (CP) tensor decomposition is frequently used as a model in applications in a variety of different fields. Using jackknife resampling to estimate parameter uncertainties is often desirable but results in an increase of the already high computational cost. Upon observation that the resampled tensors, though different, are nearly identical, we show that it is possible to extend the recently proposed Concurrent ALS (CALS) technique to a jackknife resampling scenario. This extension gives access to the computational efficiency advantage of CALS for the price of a modest increase (typically a few percent) in the number of floating point operations. Numerical experiments on both synthetic and real-world datasets demonstrate that the new workflow based on a CALS extension can be several times faster than a straightforward workflow where the jackknife submodels are processed individually.
KW - cs.MS
KW - cs.NA
KW - math.NA
U2 - 10.3389/fams.2022.830270
DO - 10.3389/fams.2022.830270
M3 - Journal article
VL - 8
JO - Frontiers in Applied Mathematics and Statistics
JF - Frontiers in Applied Mathematics and Statistics
SN - 2297-4687
M1 - 830270
ER -
ID: 305112283