Abstract
Extreme learning machines (ELM) is a state-of-the-art classification algorithm. Many applications and ELM modified versions have been proposed in recent years. We propose a frame work of semi-supervised ELM (SELM) based on SELM (Liu et al. in Neurocomputing 74:2566–2572, 2011). In this paper, we research the SELM intensively and extend SELM to ESELM (extended SELM). Compared with SELM, ESLEM considers the empirical risk and structural risk at the same time. Furthermore, we integrate LLE graph/\(l\)1 graph which were proposed recently into ESELM and compare them to the classic Laplace graph. The real-world datasets experiments indicate the effectiveness of ESELM.
Similar content being viewed by others
References
Liu J-F, Chen Y-Q, Liu M-J, Zhao Z-T (2011) SELM: semi-supervised ELM with application in sparse calibrated location estimation. Neurocomputing 74:2566–2572
Huang G-B, Chen Y-Q, Babri HA (2000) Classification ability of single hidden layer feedforward neural networks. IEEE Trans Neural Netw 11(3):799–801
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501
Huang G-B, Zhou H-M et al (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529
Zong W-W, Huang G-B (2011) Face recognition based on extreme learning machine. Neurocomputing 74(16):2541–2551
Huang G-B, Chen L, Siew C-K (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Networks 17(4):879–892
Huang G-B, Chena Lei (2007) Convex incremental extreme learning machine. Neurocomputing 70(16–18):3056–3062
Tang X-L, Min H (2009) Partial Lanczos extreme learning machine for single-output regression problems. Neurocomputing 72(13-15):3066–3076
Liu Q, He Q, Shi Z (2008) Extreme support vector machine classifier. Springer, Osaka
Cortes C, Vapnik V (1995) Support vector networks [J]. Mach Learn 20(3):273–297
He Q, Shang T-F, Zhuang F-Z, Shi Z-Z (2013) Parallel extreme learning machine for regression based on MapReduce. Neurocomputing 102:52–58
He Q, Du C-Y, Wang Q, Zhuang F-Z, Shi Z-Z (2011) A parallel incremental extreme SVM classifier. Neurocomputing 74(16):2532–2540
Belkin M, Matveeva I, Niyogi P (2004) Tikhonov regularization and semi-supervised learning on large graphs. IEEE Int Conf Acoust Speech Signal Process 3:1000–1003
Yan S-C, Wang H (2009) Semi-supervised Learning by sparse representation. SIAM international conference on data mining, pp 792–801
Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326
Wang F, Wang J-D, Zhang C-S, Shen Helen C (2006) Semi-supervised classification using linear neighborhood propagation. IEEE computer society conference on computer vision and pattern recognition, vol 1, pp 160–167
Kunlun LI, Juan ZHANG, Hongyu XU, Shangzong LUO, Hexin LI (2013) A semi-supervised extreme learning machine method based on co-training. J Comput Inf Syst 9(1):207–214
Cozman FG, Cohen I, Cirelo MC (2003) Semi-supervised learning of mixture models. ICML, pp 99–106
Cong G, Lee WS, Wu H et al (2004) Semi-supervised text classification using partitioned EM, database systems for advanced applications. Springer, Berlin
Liu SL, Feng L, Xiao Y, Wang HB. Robust activation function and its application: semi-supervised Kernel extreme learning method. Neurocomputing. doi:10.1016/j.neucom.2014.04.041
Huang G, Song S, JND, Gupta, Wu C (2014) Semi-supervised and unsupervised extreme learning machines. IEEE Trans Cybern (online)
Zhang Z, Zha H (2004) Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM J Sci Comput 26(1):313–338
Li Y-F, Zhou Z-H (2010) S4VM: safe semi-supervised support vector machine. In: CoRR, abs/1005.1545
Acknowledgments
This work was supported by National Natural Science Foundation of P.R. China (61173163, 51105052, 61370200) and Liaoning Provincial Natural Science Foundation of China (Grant No. 201102037). The authors would like to thank the reviewers for their comments which has improved the quality of the work.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, S., Feng, L., Wang, H. et al. Extend semi-supervised ELM and a frame work. Neural Comput & Applic 27, 205–213 (2016). https://doi.org/10.1007/s00521-014-1713-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-014-1713-y