Skip to Main content Skip to Navigation
Journal articles

Fast Decomposition of Large Nonnegative Tensors

Jérémy Cohen 1 Rodrigo Cabral Farias 1 Pierre Comon 1, *
* Corresponding author
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.
Complete list of metadatas

Cited literature [19 references]  Display  Hide  Download
Contributor : Pierre Comon <>
Submitted on : Friday, September 26, 2014 - 5:45:08 PM
Last modification on : Wednesday, October 14, 2020 - 1:56:03 PM
Long-term archiving on: : Friday, April 14, 2017 - 4:45:14 PM


Files produced by the author(s)




Jérémy 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. ⟨10.1109/LSP.2014.2374838⟩. ⟨hal-01069069⟩



Record views


Files downloads