Bidimensionally partitioned sequential regularized extreme learning machine

被引：0
|
作者：
Guo W. [1 ,2 ]
Xu T. [1 ,3 ]
Yu J.-J. [2 ]
Tang K.-M. [2 ]
机构：
[1] School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] School of Information Engineering, Yancheng Teachers University, Yancheng
[3] School of Computer Science and Technology, Civil Aviation University of China, Tianjin
来源：
Kongzhi yu Juece/Control and Decision | 2017年 / 32卷 / 09期
关键词：
Big data stream; Online learning; OSELM; Partitioning; Tikhonov regularization;
D O I：
10.13195/j.kzyjc.2016.0825
中图分类号：
学科分类号：
摘要：
To solve the large-scale online learning problem, this paper proposes a bidimensionally partitioned sequential regularized extreme learning machine (BP-SRELM). Based on the online sequential extreme learning machine, combining the divide-and-conquer strategy, the BP-SRELM partitions a high-dimensional hidden layer output matrix into several small matrices from the aspects of instance dimension and feature dimension, so as to reduce the scale and the complexity of the problem, and consequently, the execution efficiency of the algorithm for large-scale learning problem is significantly improved. Meanwhile, the Tikhonov regularization technology is incorporated in the BP-SRELM to further enhance the stability and the generalization capability of the algorithm in real applications. Experimental results show that the proposed BP-SRELM can provide better performances in the sense of stability and prediction accuracy with greatly improved leaning speed than its counterparts, and it can be applied to the online learning and real-time modeling of large-scale data streams. © 2017, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码：1556 / 1564
页数：8
相关论文
共 16 条
  • [1] Liang N.Y., Huang G.B., Saratchandran P., Et al., A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Trans on Neural Networks, 17, 6, pp. 1411-1423, (2006)
  • [2] Huang G.B., Zhu Q.Y., Siew C.K., Extreme learning machine: Theory and applications, Neurocomputing, 70, 1, pp. 489-501, (2006)
  • [3] Mao W.T., Tian Y.Y., Wang J.W., Et al., Granular extreme learning machine for sequential imbalanced data, Control and Decision, 31, 12, pp. 2147-2154, (2016)
  • [4] Wang X., Han M., Online sequential extreme learning machine with kernels for nonstationary time series prediction, Neurocomputing, 145, pp. 90-97, (2014)
  • [5] Lima A.R., Cannon A.J., Hsieh W.W., Forecasting daily streamflow using online sequential extreme learning machines, J of Hydrology, 537, pp. 431-443, (2016)
  • [6] Zhou H., Huang G.B., Lin Z., Et al., Stacked extreme learning machines, IEEE Trans on Cybernetics, 45, 9, pp. 2013-2025, (2015)
  • [7] Lim J.S., Partitioned online sequential extreme learning machine for large ordered system modeling, Neurocomputing, 102, 2, pp. 59-64, (2013)
  • [8] Wang B., Huang S., Qiu J., Et al., Parallel online sequential extreme learning machine based on MapReduce, Neurocomputing, 149, pp. 224-232, (2015)
  • [9] Lecun Y., Bengio Y., Hinton G., Deep learning, Nature, 521, 7553, pp. 436-444, (2015)
  • [10] Kasun L.L.C., Zhou H., Huang G.B., Et al., Representational learning with extreme learning machine for big data, IEEE Intelligent Systems, 28, 6, pp. 31-34, (2013)
12