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Title: Neural networks for efficient nonlinear online clustering
Authors: Bahroun, Yanis
Hunsicker, Eugenie
Soltoggio, Andrea
Keywords: Nonlinear kernel
Hebbian learning
Neural networks
Issue Date: 2017
Publisher: © Springer
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.
Abstract: 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.
Description: 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.
Version: Accepted for publication
DOI: 10.1007/978-3-319-70087-8_34
URI: https://dspace.lboro.ac.uk/2134/26825
Publisher Link: https://doi.org/10.1007/978-3-319-70087-8_34
ISBN: 9783319700861
Appears in Collections:Conference Papers and Presentations (Maths)
Conference Papers and Presentations (Computer Science)

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