TY - CONF A1 - Gregorová, Magda A1 - Ramapuram, Jason A1 - Kalousis, Alexandros A1 - Marchand-Maillet, Stéphane A2 - Berlingerio, Michele A2 - Bonchi, Francesco A2 - Gärtner, Thomas A2 - Hurley, Neil A2 - Ifrim, Georgiana T1 - Large-Scale Nonlinear Variable Selection via Kernel Random Features T2 - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part II N2 - 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. Y1 - 2018 UR - https://opus4.kobv.de/opus4-fhws/frontdoor/index/index/docId/4962 VL - 11052 SP - 177 EP - 192 ER -