首页|Efficient Nonnegative Tensor Decomposition Using Alternating Direction Proximal Method of Multipliers

Efficient Nonnegative Tensor Decomposition Using Alternating Direction Proximal Method of Multipliers

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Nonnegative CANDECOMP/PARAFAC(NCP)tensor decomposition is a powerful tool for multiway signal processing.The alternating direction method of multipliers(ADMM)optimization algorithm has become in-creasingly popular for solving tensor decomposition problems in the block coordinate descent framework.However,the ADMM-based NCP algorithm suffers from rank deficiency and slow convergence for some large-scale and highly sparse tensor data.The proximal algorithm is preferred to enhance optimization algorithms and improve convergence properties.In this study,we propose a novel NCP algorithm using the alternating direction proximal method of mul-tipliers(ADPMM)that consists of the proximal algorithm.The proposed NCP algorithm can guarantee convergence and overcome the rank deficiency.Moreover,we implement the proposed NCP using an inexact scheme that alterna-tively optimizes the subproblems.Each subproblem is optimized by a finite number of inner iterations yielding fast computation speed.Our NCP algorithm is a hybrid of alternating optimization and ADPMM and is named A2DPMM.The experimental results on synthetic and real-world tensors demonstrate the effectiveness and efficiency of our pro-posed algorithm.

Tensor decompositionNonnegative CANDECOMP/PARAFACAlternating direction proximal method of multipliersProximal algorithmSparse regularization

Deqing WANG、Guoqiang HU

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State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China

Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110169,China

Key Laboratory of Marine Robotics,Liaoning Province,Shenyang 110169,China

College of Artificial Intelligence,Dalian Maritime University,Dalian 116026,China

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State Key Laboratory of RoboticsNatural Science Foundation of Liaoning ProvinceDalian University of Technology

2023-Z042022-BS-029

2024

10.23919/cje.2023.00.035

电子学报(英文)

电子学报(英文)

CSTPCDEI
ISSN：1022-4653
年,卷(期)：2024.33(5)