Fast Decomposition of Large Nonnegative Tensors

Jérémy E. Cohen 1 Rodrigo Cabral Farias 1 Pierre Comon 1, *
* Auteur correspondant
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.
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IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2015, 22 (7), pp.862-866. 〈http://www.signalprocessingsociety.org/publications/periodicals/letters/〉. 〈10.1109/LSP.2014.2374838〉
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Jérémy E. Cohen, Rodrigo Cabral Farias, Pierre Comon. Fast Decomposition of Large Nonnegative Tensors. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2015, 22 (7), pp.862-866. 〈http://www.signalprocessingsociety.org/publications/periodicals/letters/〉. 〈10.1109/LSP.2014.2374838〉. 〈hal-01069069〉

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