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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Published in | IEEE signal processing letters Vol. 22; no. 7; pp. 862 - 866 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.07.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers |
Subjects | |
Online Access | Get full text |
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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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1070-9908 1558-2361 |
DOI: | 10.1109/LSP.2014.2374838 |