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Deqing WANG and Guoqiang HU, “Efficient nonnegative tensor decomposition using alternating direction proximal method of multipliers,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–9, xxxx doi: 10.23919/cje.2023.00.035
Citation: Deqing WANG and Guoqiang HU, “Efficient nonnegative tensor decomposition using alternating direction proximal method of multipliers,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–9, xxxx doi: 10.23919/cje.2023.00.035

Efficient nonnegative tensor decomposition using alternating direction proximal method of multipliers

doi: 10.23919/cje.2023.00.035
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  • Author Bio:

    Deqing WANG received the B.E. degree in automation and the M.E. degree in pattern recognition and intelligent system from Harbin Engineering University, Harbin, China, in 2009 and 2012, respectively. He received his Ph.D. degree in 2019 from the University of Jyväskylä, Jyväskylä, Finland. He was appointed as an assistant engineer and an engineer at Dalian Scientific Test and Control Technology Institute, China Shipbuilding Industry Corporation (CSIC), Dalian, China, in 2012 and 2014, respectively. He is currently an assistant professor at Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China. His research interests include signal processing, machine learning, tensor decomposition, robotics and intelligent systems. (Email: deqing.wang@foxmail.com)

    Guoqiang HU received the B.E. degree and the Ph.D. degree in biomedical engineering from Dalian University of Technology, Dalian, China, in 2015 and 2022, respectively. He was a visiting doctoral student at Harvard Medical School, Boston, MA, USA, from 2018 to 2020. He is currently a lecturer at the College of Artificial Intelligence, Dalian Maritime University, Dalian, China. His research interests include brain signal analysis and processing, independent component analysis, tensor decomposition and artificial intelligence. (Email: guoqiang.hu@dlmu.edu.cn)

  • Corresponding author: Email: deqing.wang@foxmail.com
  • Available Online: 2023-12-13
  • Nonnegative CANDECOMP/PARAFAC (NCP) tensor decomposition is a powerful tool for multiway signal processing. The optimization algorithm alternating direction method of multipliers (ADMM) has become increasingly popular for solving tensor decomposition problems in the block coordinate descent framework. However, the ADMM-based NCP 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 multipliers (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 alternatively 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 proposed algorithm.
  • 1http://www.svcl.ucsd.edu/projects/anomaly/dataset.html
    3The spatial components were plotted using the software REST [34]. REST can be downloaded from http://www.rfmri.org/REST.
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