Fast Decomposition of Large Nonnegative Tensors

被引：36

作者：

Cohen, Jeremy ^{[1
]}

Farias, Rodrigo Cabral ^{[1
]}

Comon, Pierre ^{[1
]}

机构：

[1] CNRS, Images & Signal Dept, GIPSA Lab, F-38402 St Martin Dheres, France

来源：

IEEE SIGNAL PROCESSING LETTERS | 2015年 / 22卷 / 07期

关键词：

Big Data; compression; CP decomposition; HOSVD; nonnegative; PARAFAC; tensor; PARALLEL FACTOR-ANALYSIS; MATRIX;

D O I：

10.1109/LSP.2014.2374838

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：862 / 866

页数：5

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