Kernel Extreme Learning Machine Based on Alternating Direction Multiplier Method of Binary Splitting Operator

Yidan SU; Jia XU; Hua QIN

doi:10.11999/JEIT200884

Volume 43 Issue 9

Sep. 2021

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Yidan SU, Jia XU, Hua QIN. Kernel Extreme Learning Machine Based on Alternating Direction Multiplier Method of Binary Splitting Operator[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2586-2593. doi: 10.11999/JEIT200884

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Yidan SU, Jia XU, Hua QIN. Kernel Extreme Learning Machine Based on Alternating Direction Multiplier Method of Binary Splitting Operator[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2586-2593. doi: 10.11999/JEIT200884

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Kernel Extreme Learning Machine Based on Alternating Direction Multiplier Method of Binary Splitting Operator

doi: 10.11999/JEIT200884 cstr: 32379.14.JEIT200884

College of Computer and Electronic Information, Guangxi University, Nanning 530004, China

Received Date: 2020-10-16
Rev Recd Date: 2021-01-31

Available Online: 2021-03-01

Publish Date: 2021-09-16

Abstract

Abstract

The Kernel Extreme Learning Machine (KELM) with convex optimization form has higher classification accuracy, but it takes longer time to train kelm with iterative method than solving linear equation method of traditional kelm. To solve this problem, an Alternating Direction Multiplier Method(ADMM) of Binary Splitting (BSADMM-KELM) is proposed to improve the training speed of convex optimization kernel extreme learning machine. Firstly, the process of finding the optimal solution of the kernel extreme learning machine is split into two intermediate operators by introducing a binary splitting operator, and then the optimal solution of the original problem is obtained through the iterative calculation of the intermediate operators. On 22 UCI datasets, the training time of the proposed algorithm is 29 times faster than that of the effective set method and 4 times faster than that of the interior point method. The classification accuracy of the proposed algorithm is also better than that of the traditional kernel extreme learning machine. On large-scale datasets, the training time of the proposed algorithm is better than that of the traditional kernel extreme learning machine.
- Kernel Extreme Learning Machine(KELM),
- Quadratic programming,
- Binary splitting operator,
- Alternating Direction Multiplier Method(ADMM)

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References(20)

References

[1]	HUANG Guangbin, ZHU Qinyu, SIEW C K, et al. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1/3): 489–501. doi: 10.1016/j.neucom.2005.12.126
[2]	QING Yuanyuan, ZENG Yijie, LI Yue, et al. Deep and wide feature based extreme learning machine for image classification[J]. Neurocomputing, 2020, 412: 426–436. doi: 10.1016/j.neucom.2020.06.110
[3]	CHEN Xudong, YANG Haiyue, WUN Jhangshang, et al. Power load forecasting in energy system based on improved extreme learning machine[J]. Energy Exploration & Exploitation, 2020, 38(4): 1194–1211. doi: 10.1177/0144598720903797
[4]	KHAN M A, ABBAS S, KHAN K M, et al. Intelligent forecasting model of COVID-19 novel coronavirus outbreak empowered with deep extreme learning machine[J]. Computers, Materials & Continua, 2020, 64(3): 1329–1342. doi: 10.32604/cmc.2020.011155
[5]	MUTHUNAYAGAM M and GANESAN K. Cardiovascular disorder severity detection using myocardial anatomic features based optimized extreme learning machine approach[J]. IRBM, To be published. doi: 10.1016/j.irbm.2020.06.004
[6]	WU Yu, ZHANG Yongshan, LIU Xiaobo, et al. A multiobjective optimization-based sparse extreme learning machine algorithm[J]. Neurocomputing, 2018, 317: 88–100. doi: 10.1016/j.neucom.2018.07.060
[7]	ZHANG Yongshan, WU Jia, CAI Zhihua, et al. Memetic extreme learning machine[J]. Pattern Recognition, 2016, 58: 135–148. doi: 10.1016/j.patcog.2016.04.003
[8]	PENG Yong, KONG Wanzeng, and YANG Bing. Orthogonal extreme learning machine for image classification[J]. Neurocomputing, 2017, 266: 458–464. doi: 10.1016/j.neucom.2017.05.058
[9]	ZUO Bai, HUANG Guangbin, WANG Danwei, et al. Sparse extreme learning machine for classification[J]. IEEE Transactions on Cybernetics, 2014, 44(10): 1858–1870. doi: 10.1109/TCYB.2014.2298235
[10]	GHADIMI E, TEIXEIRA A, SHAMES I, et al. Optimal parameter selection for the alternating direction method of multipliers (ADMM): Quadratic problems[J]. IEEE Transactions on Automatic Control, 2015, 60(3): 644–658. doi: 10.1109/TAC.2014.2354892
[11]	LIU Yuanyuan, SHANG Fanhua, and CHENG J. Accelerated variance reduced stochastic ADMM[C].The Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, USA, 2017: 2287–2293.
[12]	DHAR S, YI Congrui, RAMAKRISHNAN N, et al. ADMM based scalable machine learning on Spark[C]. 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, USA, 2015.
[13]	STELLATO B, BANJAC G, GOULART P, et al. OSQP: An operator splitting solver for quadratic programs[J]. Mathematical Programming Computation, 2020, 12(4): 637–672. doi: 10.1007/s12532-020-00179-2
[14]	HUANG Guangbin, DING Xiaojian, and ZHOU Hongming. Optimization method based extreme learning machine for classification[J]. Neurocomputing, 2010, 74(1/3): 155–163. doi: 10.1016/j.neucom.2010.02.019
[15]	HUYER W and NEUMAIER A. MINQ: General definite and bound constrained indefinite quadratic programming[EB/OL]. https://www.mat.univie.ac.at/~neum/software/minq/, 2017.
[16]	ZHANG Wenyu, ZHANG Zhenjiang, WANG Lifu, et al. Extreme learning machines with expectation kernels[J]. Pattern Recognition, 2019, 96: 106960. doi: 10.1016/j.patcog.2019.07.005
[17]	WAN Yihe, SONG Shiji, HUANG Gao, et al. Twin extreme learning machines for pattern classification[J]. Neurocomputing, 2017, 260: 235–244. doi: 10.1016/j.neucom.2017.04.036
[18]	DING Xiaojian, LAN Yuan, ZHANG Zhifeng, et al. Optimization extreme learning machine with ν regularization[J]. Neurocomputing, 2017, 261: 11–19. doi: 10.1016/j.neucom.2016.05.114
[19]	YANG Liming and ZHANG Siyun. A sparse extreme learning machine framework by continuous optimization algorithms and its application in pattern recognition[J]. Engineering Applications of Artificial Intelligence, 2016, 53: 176–189. doi: 10.1016/j.engappai.2016.04.003
[20]	LI Shuang, SONG Shiji, and WAN Yihe. Laplacian twin extreme learning machine for semi-supervised classification[J]. Neurocomputing, 2018, 321: 17–27. doi: 10.1016/j.neucom.2018.08.028