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Neural networks for efficient nonlinear online clustering

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conference contribution
posted on 05.10.2017, 13:26 by Yanis Bahroun, Eugenie HunsickerEugenie Hunsicker, Andrea SoltoggioAndrea Soltoggio
Unsupervised learning techniques, such as clustering and sparse coding, have been adapted for use with data sets exhibiting nonlinear relationships through the use of kernel machines. These techniques often require an explicit computation of the kernel matrix, which becomes expensive as the number of inputs grows, making it unsuitable for efficient online learning. This paper proposes an algorithm and a neural architecture for online approximated nonlinear kernel clustering using any shift-invariant kernel. The novel model outperforms traditional low-rank kernel approximation based clustering methods, it also requires significantly lower memory requirements than those of popular kernel k-means while showing competitive performance on large data sets.

History

School

  • Science

Department

  • Computer Science

Published in

International Conference on Neural Information Processing

Citation

BAHROUN, Y., HUNSICKER, E. and SOLTOGGIO, A., 2017. Neural networks for efficient nonlinear online clustering. IN: Liu, D. ...et al. (eds.) Neural Information Processing: 24th International Conference on Neural Information Processing (ICONIP 2017), Guangzhou, China, 14-18 November 2017, pp.316-324.

Publisher

© Springer

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

31/07/2017

Publication date

2017

Notes

This is a pre-copyedited version of a contribution published in Liu, D. ...et al. (eds.) Neural Information Processing: 24th International Conference on Neural Information Processing (ICONIP 2017) published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-319-70087-8_34.

ISBN

9783319700861

Language

en

Location

Guangzhou, China