Abstract
Usually, the solution of the conventional extreme learning machine, which is a type of single-hidden-layer feedforward neural networks, is not sparse.
In this paper, to overcome this problem, we discuss a sparse extreme learning machine using empirical feature mapping. Here, the basis vectors of empirical feature space are the linearly independent training vectors. Then, unlike the conventional extreme learning machine, only these linearly independent training vectors become support vectors. Hence, the solution of the proposed method is sparse. Using UCI bench-mark datasets, we evaluate the effectiveness of the proposed method over the conventional methods from the standpoints of the sparsity and classification capability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: International Joint Conference on Neural Networks (IJCNN 2004), vol. 2, pp. 985–990 (2004)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and application. Neurocomputing 70(1–3), 489–501 (2006)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Cybern. 42(2), 513–529 (2012)
Abe, S.: Support Vector Machines for Pattern Classification (Advances in Pattern Recognition). Springer-Verlag, London (2010)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Huang, G.B., Ding, X., Zhou, H., Westover, M.B.: Sparse extreme learnin machine for classification. Neurocomputing 74(1–3), 155–163 (2010)
Bai, Z., Huang, G.B., Wang, D., Wang, H.: Optimization method based extreme learning machine for classification. IEEE Trans. Cybern. 44(10), 1858–1870 (2014)
Abe, S.: Sparse least squares support vector training in the reduced empirical feature space. Pattern Anal. Appl. 10(3), 203–214 (2007)
Xiong, H., Swamy, M.N.S., Ahmad, M.O.: Optimizing the Kernel in the empirical feature space. IEEE Trans. Neural Netw. 16(2), 460–474 (2005)
Kitamura, T., Sekine, T.: A novel method of sparse least squares support vector machines in class empirical feature space. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part II. LNCS, vol. 7664, pp. 475–482. Springer, Heidelberg (2012)
Kitamura, T., Takeuchi, S., Abe, S., Fukui, K.: Subspace-based support vector machines for pattern classification. Neural Netw. 22, 558–567 (2009)
Kitamura, T., Takeuchi, S., Abe, S.: Feature selection and fast training of subspace based support vector machines. In: International Joint Conference on Neural Networks (IJCNN 2010), pp. 1967–1972 (2010)
Bartlett, P.L.: The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans. Inf. Theor. 44(2), 525–536 (1998)
Rätsch, G., Onda, T., Müller, K.R.: Soft margins for AdaBoost. Mach. Learn. 42(3), 287–320 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kitamura, T. (2016). Sparse Extreme Learning Machine Classifier Using Empirical Feature Mapping. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_57
Download citation
DOI: https://doi.org/10.1007/978-3-319-44778-0_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44777-3
Online ISBN: 978-3-319-44778-0
eBook Packages: Computer ScienceComputer Science (R0)