Deep Alternating Direction Multiplier Method Network for Event Detection

HU Shicheng; YANG Liu; KANG Kai; QIAN Hua

doi:10.11999/JEIT220744

Volume 45 Issue 7

Jul. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(7): 2634-2641

HU Shicheng, YANG Liu, KANG Kai, QIAN Hua. Deep Alternating Direction Multiplier Method Network for Event Detection[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2634-2641. doi: 10.11999/JEIT220744

Citation:

HU Shicheng, YANG Liu, KANG Kai, QIAN Hua. Deep Alternating Direction Multiplier Method Network for Event Detection[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2634-2641. doi: 10.11999/JEIT220744

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Deep Alternating Direction Multiplier Method Network for Event Detection

doi: 10.11999/JEIT220744

HU Shicheng^{1, 2, 3},
YANG Liu⁴,
KANG Kai¹,
QIAN Hua^{1, 2, 3
,
,}

1.
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
2.
School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China
4.
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China

Funds: The National Natural Science Foundation of China (61971286), The National Key Research and Development Program of China (2020YFB2205603), The Science and Technology Commission Foundation of Shanghai (19DZ1204300)

Received Date: 2022-06-07
Rev Recd Date: 2022-10-05

Available Online: 2022-10-11

Publish Date: 2023-07-10

Abstract

Abstract

Considering the Event Detection Problem (EDP) in the large-scale Wireless Sensor Network (WSN), the conventional methods rely generally on some prior information, which obstacles the actual application. In this paper, a deep learning-based algorithm, named as Alternating Direction Multiplier Method Network (ADMM-Net), is proposed for the EDP. Firstly, the low rank and sparse matrix decomposition is adopted to capture the spatial-temporal correlation of events. After that, the EDP is formulated as a constrained optimization problem and solved by the Alternating Direction Multiplier Method (ADMM). However, the optimization algorithm suffers from low convergence. Besides, the algorithm’s performance relies heavily on the careful selection of prior parameters. By adopting the conception of “unfolding” in deep learning field, a deep learning network which is named ADMM-Net, is proposed for the EDP in this paper. The ADMM-Net is obtained by unfolding the ADMM algorithm. The ADMM-Net is with fixed layers, whose parameters can be trained via supervised learning. No prior information is required. Compared to the conventional methods, the proposed ADMM-Net does not require any prior information while enjoying fast convergence. Simulation results on both synthesis and realistic datasets verify the effectiveness of the proposed ADMM-Net.

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References(19)

References

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