Abstract
The extreme learning machine (ELM) provides efficient unified solutions for generalized single hidden layer feed-forward neural networks. Hierarchical learning based on ELM has now attracted lots of interests. This paper presents a hierarchical pruning discriminative ELM (H-PDELM) for feature learning and classification. The ELM pruning auto-encoder (ELM-PAE) is developed for unsupervised feature learning by promoting the output weights matrix to be row-sparse based on l2, 1-norm regularization. ELM-PAE can naturally distinguish and prune useless neurons in hidden layer to determine the structure of AE. Besides, we learn a flexible output weights matrix for supervised feature classification by relaxing the strict regression label matrix of ELM into a slack one for better generalization performance. H-PDELM performs layer-wise unsupervised feature learning using ELM-PAE, and conducts decision making by the flexible output weights matrix. The network of H-PDELM is compact with good generalization ability. Preliminary experiments on visual dataset show its effectiveness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Huang, G., Huang, G.-H., Song, G., Youa, K.: Trends in extreme learning machines: a review. Neural Netw. 61(C), 32–48 (2015)
Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. B Cybern. 42(2), 513–529 (2012)
Huang, G.-B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Huang, G.-B., Chen, L.: Convex incremental extreme learning machine. Neurocomputing 70(16–18), 3056–3062 (2007)
Huang, G., Song, S., Gupta, J., Wu, C.: Semi-supervised and unsupervised extreme learning machines. IEEE Trans. Cybern. 44(12), 2405 (2014)
Kasun, L.L., Yang, Y., Huang, G.B., et al.: Dimension reduction with extreme learning machine. IEEE Trans. Image Process. 25(8), 3906 (2016)
Zhang, L., Zhang, D.: Robust visual knowledge transfer via extreme learning machine based domain adaptation. IEEE Trans. Image Process. 25(10), 4959–4973 (2016)
Zhang, L., Zhang, D.: Domain adaptation extreme learning machines for drift compensation in E-nose systems. IEEE Trans. Instrum. Meas. 64(7), 1790–1801 (2015)
Zhang, L., Zhang, D.: Evolutionary cost-sensitive extreme learning machine. IEEE Trans. Neural Netw. & Learn. Syst. (99), 1–16 (2015)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nat. 521(7553), 436–444 (2015)
Tang, J., Deng, C., Huang, G.B.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. & Learn. Syst. 27(4), 809–821 (2016)
Guo, T., Zhang, L., Tan, X.: Neuron pruning-based discriminative extreme learning machine for pattern classification. Cogn. Comput. 1–15 (2017)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Guo, T., Tan, X., Zhang, L. (2019). Hierarchical Pruning Discriminative Extreme Learning Machine. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM-2017. ELM 2017. Proceedings in Adaptation, Learning and Optimization, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-01520-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-01520-6_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01519-0
Online ISBN: 978-3-030-01520-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)