Accelerating jackknife resampling for the Canonical Polyadic Decomposition
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.
Authors 4
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Christos Psarras Aachen International Research Training Group Aachen Institute for Advanced Study in Computational Engineering Science (AICES) Department of Computer Science
Aachen Institute for Advanced Study in Computational Engineering Science · RWTH Aachen University
Affiliation as printed
International Research Training Group, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), Department of Computer Science, RWTH Aachen University, Aachen, Germany
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Affiliation as printed
High-Performance and Automatic Computing Group, Department of Computing Science, Umeå University, Umeå, Sweden
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Affiliation as printed
Department of Food Science, Institute for Fødevarevidenskab, University of Copenhagen, Copenhagen, Denmark
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Paolo Bientinesi corresponding
Affiliation as printed
High-Performance and Automatic Computing Group, Department of Computing Science, Umeå University, Umeå, Sweden
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