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Title

Large-scale Nonlinear Variable Selection via Kernel Random Features

Authors
Published in Berlingerio, Michele ; Bonchi, Francesco ; Gärtner, Thomas ; Hurley, Neil & Ifrim, Georgiana. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Proceedings. Part II. Dublin (Ireland) - 10-14 September 2018 - Cham: Springer. 2019, p. 177-192
Collection Lecture Notes in Computer Science; 11052
Abstract 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.
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ISBN: 9783030109288
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Research groups Computer Vision and Multimedia Laboratory
Viper group
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GREGOROVA, Magda et al. Large-scale Nonlinear Variable Selection via Kernel Random Features. In: Berlingerio, Michele ; Bonchi, Francesco ; Gärtner, Thomas ; Hurley, Neil & Ifrim, Georgiana (Ed.). Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Proceedings. Part II. Dublin (Ireland). Cham : Springer, 2019. p. 177-192. (Lecture Notes in Computer Science; 11052) https://archive-ouverte.unige.ch/unige:129030

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Deposited on : 2020-01-17

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