Fast Decomposition of Large Nonnegative Tensors

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 a...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 22; no. 7; pp. 862 - 866
Main Authors Cohen, Jeremy E., Farias, Rodrigo Cabral, Comon, Pierre
Format Journal Article
LanguageEnglish
Published New York IEEE 01.07.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects
Approximation methods
Big data
Compressed
compression
Computer Science
Convergence
CP decomposition
Data compression
Decomposition
Engineering Sciences
Handles
HOSVD
Image coding
Linear programming
Mathematical analysis
nonnegative
PARAFAC
Signal and Image Processing
Signal processing
Signal processing algorithms
Tensile stress
tensor
Tensors
HOSVD
Compression
Non-negative
Tensor
Parafac
Big Data
CP decomposition
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Summary: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.
Bibliography:ObjectType-Article-1
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2014.2374838