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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Vydáno v:IEEE transactions on signal processing Ročník 63; číslo 20; s. 5450 - 5463
Hlavní autoři: Liavas, Athanasios P., Sidiropoulos, Nicholas D.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.10.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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 computation on a single machine; and the few that break away from this mold do not easily incorporate practically important constraints, such as non-negativity. A new constrained tensor factorization framework is proposed in this paper, building upon the Alternating Direction Method of Multipliers (ADMoM). It is shown that this simplifies computations, bypassing the need to solve constrained optimization problems in each iteration; and it naturally leads to distributed algorithms suitable for parallel implementation. This opens the door for many emerging big data-enabled applications. The methodology is exemplified using non-negativity as a baseline constraint, but the proposed framework can incorporate many other types of constraints. Numerical experiments are encouraging, indicating that ADMoM-based non-negative tensor factorization (NTF) has high potential as an alternative to state-of-the-art approaches.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2015.2454476