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* Corresponding author 1 GIPSA-CICS - CICS GIPSA-DIS - Département Images et Signal

Abstract : In Signal processing, tensor decompositions have gained in popularity this last decade.
In the meantime, the volume of data to be processed has drastically increased.
This calls for novel methods to handle Big Data tensors.
Since most of these huge data are issued from physical measurements, which are intrinsically real nonnegative, being able to compress nonnegative tensors has become mandatory.
Following recent works on HOSVD compression for Big Data, we detail solutions to decompose a nonnegative tensor into decomposable terms in a compressed domain.


keyword : Big Data Compression CP decomposition HOSVD Non-negative Parafac Tensor





Autor: Jérémy E.
Cohen - Rodrigo Cabral Farias - Pierre Comon -


Fuente: https://hal.archives-ouvertes.fr/



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