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International Conference on Extreme Learning Machine

ELM 2018: Proceedings of ELM 2018 pp 1–10Cite as

Random Orthogonal Projection Based Enhanced Bidirectional Extreme Learning Machine

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Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 11))

Abstract

Bidirectional extreme learning machine (B-ELM) divides the learning process into two parts: At odd learning step, the parameters of the new hidden node are generated randomly, while at even learning step, the parameters of the new hidden node are obtained analytically from the parameters of the former node. However, some of the odd-hidden nodes play a minor role, which will have a negative impact on the even-hidden nodes, and result in a sharp rise in the network complexity. To avoid this issue, we propose a random orthogonal projection based enhanced bidirectional extreme learning machine algorithm (OEB-ELM). In OEB-ELM, several orthogonal candidate nodes are generated randomly at each odd learning step, only the node with the largest residual error reduction will be added to the existing network. Experiments on six real datasets have shown that the OEB-ELM has better generalization performance and stability than B-ELM, EB-ELM, and EI-ELM algorithms.

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References

  1. Cao, W.P., Wang, X.Z., Ming, Z., Gao, J.Z.: A review on neural networks with random weights. Neurocomputing 275, 278–287 (2018)

    Article  Google Scholar 

  2. Cao, J.W., Lin, Z.P.: Extreme learning machines on high dimensional and large data applications: a survey. Math. Prob. Eng. 2015, 1–13 (2015)

    MATH  Google Scholar 

  3. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine a new learning scheme of feedforward neural networks. In: Proceedings of 2004 IEEE international joint conference on neural networks, vol. 2, pp. 985–990 (2004)

    Google Scholar 

  4. Zhang, L., Zhang, D.: Evolutionary cost-sensitive extreme learning machine. IEEE Trans. Neural Netw. Learn. Syst. 28(12), 3045–3060 (2017)

    Article  MathSciNet  Google Scholar 

  5. Ding, S.F., Guo, L.L., Hou, Y.L.: Extreme learning machine with kernel model based on deep learning. Neural Comput. Appl. 28(8), 1975–1984 (2017)

    Article  Google Scholar 

  6. Zhang, H.G., Zhang, S., Yin, Y.X.: Online sequential ELM algorithm with forgetting factor for real applications. Neurocomputing 261, 144–152 (2017)

    Article  Google Scholar 

  7. He, Q., Jin, X., Du, C.Y., Zhuang, F.Z., Shi, Z.Z.: Clustering in extreme learning machine feature space. Neurocomputing 128, 88–95 (2014)

    Article  Google Scholar 

  8. Huang, Z., Yu, Y., Gu, J., Liu, H.: An efficient method for traffic sign recognition based on extreme learning machine. IEEE Trans. Cybern. 47(4), 920–933 (2017)

    Article  Google Scholar 

  9. Cao, W.P., Gao, J.Z., Ming, Z., Cai, S.B.: Some tricks in parameter selection for extreme learning machine. IOP Conf. Ser. Mater. Sci. Eng. 261(1), 012002 (2017)

    Google Scholar 

  10. Huang, G.B., Chen, L., Siew, C.K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17(4), 879–892 (2006)

    Article  Google Scholar 

  11. Huang, G.B., Chen, L.: Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16–18), 3460–3468 (2008)

    Article  Google Scholar 

  12. Yang, Y.M., Wang, Y.N., Yuan, X.F.: Bidirectional extreme learning machine for regression problem and its learning effectiveness. IEEE Trans. Neural Netw. Learn. Syst. 23(9), 1498–1505 (2012)

    Article  Google Scholar 

  13. Cao, W.P., Ming, Z., Wang, X.Z., Cai, S.B.: Improved bidirectional extreme learning machine based on enhanced random search. Memetic Comput., 1–8 (2017). https://doi.org/10.1007/s12293-017-0238-1

    Article  Google Scholar 

  14. Rong, H.J., Ong, Y.S., Tan, A.H., Zhu, Z.: A fast pruned-extreme learning machine for classification problem. Neurocomputing 72, 359–366 (2008)

    Article  Google Scholar 

  15. Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., Lendasse, A.: OPELM: optimally pruned extreme learning machine. IEEE Trans. Neural Netw. 21(1), 158–162 (2010)

    Article  Google Scholar 

  16. Wang, W.H., Liu, X.Y.: The selection of input weights of extreme learning machine: a sample structure preserving point of view. Neurocomputing 261, 28–36 (2017)

    Article  Google Scholar 

  17. Kasun, L.L.C., Zhou, H., Huang, G.B., Vong, C.M.: Representational learning with extreme learning machine for big data. IEEE Intell. Syst. 28, 31–34 (2013)

    Article  Google Scholar 

  18. Huang, G.B., Bai, Z., Kaun, L.L.C., Vong, C.M.: Local receptive fields based extreme learning machine. IEEE Comput. Intell. Mag. 10, 18–29 (2015)

    Article  Google Scholar 

  19. Blake, C., Merz, C.: UCI repository of machine learning databases. Technical report, Dept. Inf. Comput. Sci., Univ. California, Irvine, CA, USA (1998). http://archive.ics.uci.edu/ml/

Download references

Acknowledgment

This research was supported by the National Natural Science Foundation of China (61672358).

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Weipeng Cao, Xizhao Wang, Zhong Ming & Shubin Cai

  2. School of Engineering and Computer Science, University of the Pacific, Stockton, 95211, CA, USA

    Jinzhu Gao

Authors
  1. Weipeng Cao
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  2. Jinzhu Gao
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  3. Xizhao Wang
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  4. Zhong Ming
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  5. Shubin Cai
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Corresponding author

Correspondence to Zhong Ming .

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Editors and Affiliations

  1. Institute of Information and Control, Hangzhou Dianzi University, Xiasha, Hangzhou, China

    Jiuwen Cao

  2. Department of Computer and Information Science, University of Macau, Taipa, Macao

    Chi Man Vong

  3. Nokia Bell Labs, Cybersecurity Research, Espoo, Finland

    Yoan Miche

  4. Department of Information and Logistics Technology, University of Houston, Houston, TX, USA

    Amaury Lendasse

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Cao, W., Gao, J., Wang, X., Ming, Z., Cai, S. (2020). Random Orthogonal Projection Based Enhanced Bidirectional Extreme Learning Machine. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_1

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