Accelerating jackknife resampling for the Canonical Polyadic Decomposition

Christos Psarras, Lars Karlsson, Rasmus Bro, Paolo Bientinesi*

*Corresponding author for this work

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Abstract

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.
Original languageEnglish
Article number830270
JournalFrontiers in Applied Mathematics and Statistics
Volume8
Number of pages11
ISSN2297-4687
DOIs
Publication statusPublished - 2022

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