Large-Scale Nonlinear Variable Selection via Kernel Random Features

  • We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selection method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets. Code related to this paper is available at: https://bitbucket.org/dmmlgeneva/srff_pytorch.

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Metadaten
Author:Magda Gregorová, Jason Ramapuram, Alexandros Kalousis, Stéphane Marchand-Maillet
DOI:https://doi.org/10.1007/978-3-030-10928-8_11
Parent Title (English):Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part II
Editor:Michele Berlingerio, Francesco Bonchi, Thomas Gärtner, Neil Hurley, Georgiana Ifrim
Document Type:Conference Proceeding
Language:English
Year of publication:2018
Release Date:2024/01/22
Volume:11052
Pages/Size:16
First Page:177
Last Page:192
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