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| Batch Hierarchical Coding Extreme Learning Machine Based on Manifold Regularization |
| LIU Bin1,YANG You-heng1,LIU Jing1,WANG Wei-tao1,LIU Hao-ran2,WEN Yan1 |
1. Electrical Engineering College, Yanshan University, Qinhuangdao, Hebei 066004, China
2. Information Science and Engineering College, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract Aiming at the problems of high memory energy consumption,low classification accuracy and poor generalization when dealing with high-dimensional data, a batch hierarchical coding extreme learning machine algorithm is proposed.Firstly,the dataset is processed in batches to reduce the data dimension and reduce input complexity.Then,the multi-layer automatic encoder structure is used to unsupervise the batch data to achieve deep feature extraction.Finally,the manifold regularization is used to construct a manifold classifier with inheritance factors to maintain data integrity and improve the generalization performance of the algorithm.The experimental results show that the method is simple to implement,and the classification accuracy on the NORB,MNIST,and USPS datasets can reach 92.16%,99.35%,and 98.86%,respectively.Compared with other ELM algorithms,it has obvious advantages in reducing computational complexity and reducing CPU memory consumption.
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Received: 25 November 2019
Published: 15 July 2021
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［1］Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications ［J］. Neurocomputing, 2006, 70(1): 489-501.
［2］Iosifidis A, Tefas A, Pitas I. Graph embedded extreme learning machine ［J］. IEEE Transactions on Cybernetics, 2017, 46 (1): 311-324.
［3］何群, 王红, 江国乾, 等. 基于相关主成分分析和极限学习机的风电机组主轴承状态监测研究 ［J］. 计量学报, 2018, 39 (1): 89-93.
He Q, Wang H, Jiang G Q, et al. Research of wind turbine main bearing condition monitoring based on correlation PCA and ELM ［J］. Acta Metrologica Sinica, 2018, 39 (1): 89-93.
［4］吴忠强, 尚梦瑶, 申丹丹, 等. 基于BSA-RELM的纯电动汽车锂离子电池SOC估计 ［J］. 计量学报, 2019, 40 (4): 693-699.
Wu Z Q, Shang M Y, Shen D D, et al. Estimation of SOC of Li-ion battery in pure electric vehicle by BSA-RELM ［J］. Acta Metrologica Sinica, 2019, 40 (4): 693-699.
［5］牛培峰, 丁翔, 刘楠, 等. 基于混合鸡群算法和核极端学习机的锅炉NOx排放的预测 ［J］.计量学报, 2019, 40 (5): 929-936.
Niu P F, Ding X, Liu N, et al. Prediction of boiler NOx emission based on mixed chicken swarm algorithm and kernel extreme learning machines ［J］. Acta Metrologica Sinica, 2019, 40 (5): 929-936.
［6］Kasun L L C, Zhou H M, Huang G B, et al. Represen-tational learning with extreme learning machine for big data ［J］. IEEE Intelligent Systems, 2014, 28 (6): 30-59.
［7］Tang J X, Deng C W, Huang G B. Extreme learning machine for multilayer perceptron ［J］. IEEE Transac-tions on Neural Networks and Learning Systems, 2016, 27 (4): 809-821.
［8］徐睿, 梁循, 齐金山, 等. 极限学习机前沿进展与趋势 ［J］. 计算机学报, 2019, 42 (7): 1640-1670.
Xu R, Liang X, Qi J S, et al. Advances and trends in extreme learning machine ［J］.Chinese Journal of Compu-ters, 2019, 42 (7): 1640-1670.
［9］Zhou H, Huang G B, Lin Z, et al. Stacked extreme learning machines ［J］. IEEE Transactions on Cybernetics, 2015, 45 (9): 2013-2025.
［10］Vong C M, Chen C, Wong P K . Empirical kernel map-based multilayer extreme learning machines for represen-tation learning ［J］. Neurocomputing, 2018, 310: 265-276.
［11］Tang G J, Wang X L, He Y L. A novel method of fault diagnosis for rolling bearing based on dual tree complex wavelet packet transform and improved multiscale per-mutation entropy ［J］. Mathematical Problems in Engine-ering, 2016: 1-13.
［12］Huang G B, Zhou H M, DING X J, et al. Extreme learning machine for regression and multiclass classifica-tion ［J］. IEEE Transactions on Systems, Man and Cyb-ernetics, Part B (Cybernetics), 2012, 42 (2): 513-529.
［13］Beck A, Teboulle M. A fast iterative shrinkagethresh-olding algorithm for linear inverse problems ［J］. SIAM Journal on Imaging Sciences, 2009, 2 (1): 183-202.
［14］Yao L, Ge Z . Deep learning of semi-supervised process data with hierarchical extreme learning machine and soft sensor application ［J］. IEEE Transactions on Industrial Electronics, 2017, 65 (2): 1490-1498.
［15］Huang G, Song S, Gupta J N D, et al. Semi-supervised and unsupervised extreme learning machines ［J］. IEEE Transactions on Cybernetics, 2014, 44 (12): 2405-2417.
［16］张立峰,朱炎峰. 基于粒子群优化极限学习机及电容层析成像的两相流流型及其参数预测［J］.计量学报, 2020, 41 (12): 1488-1493.
Zhang L F, Zhu Y F. Two-phase Flow Regime and its Parameter Prediction Based on Particle Swarm Optimization Extreme Learning Machine and Electrical Capacitance Tomography［J］. Acta Metrologica Sinica, 2020, 41 (12): 1488-1493. |
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2021, Vol. 42 
