Parallel Algorithms for Constrained Tensor Factorization via Alternating Direction Method of Multipliers

Tensor factorization has proven useful in a wide range of applications, from sensor array processing to communications, speech and audio signal processing, and machine learning. With few recent exceptions, all tensor factorization algorithms were originally developed for centralized, in-memory compu...

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Vydané v:	IEEE transactions on signal processing Ročník 63; číslo 20; s. 5450 - 5463
Hlavní autori:	Liavas, Athanasios P., Sidiropoulos, Nicholas D.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 15.10.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Analytical models Computer architecture Matrices Optimization PARAFAC model parallel algorithms Signal processing algorithms Sparse matrices Tensile stress Tensor decomposition
ISSN:	1053-587X, 1941-0476
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