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Learning with infinitely many features

Abstract : We propose a principled framework for learning with infinitely many features, situations that are usually induced by continuously parametrized feature extraction methods. Such cases occur for instance when considering Gabor-based features in computer vision problems or when dealing with Fourier features for kernel approximations. We cast the problem as the one of finding a finite subset of features that minimizes a regularized empirical risk. After having analyzed the optimality conditions of such a problem, we propose a simple algorithm which has the avour of a column-generation technique. We also show that using Fourier-based features, it is possible to perform approximate infinite kernel learning. Our experimental results on several datasets show the benefits of the proposed approach in several situations including texture classification and large-scale kernelized problems (involving about 100 thousand examples).
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Contributor : Rémi Flamary Connect in order to contact the contributor
Submitted on : Thursday, September 27, 2012 - 11:19:08 AM
Last modification on : Thursday, August 4, 2022 - 4:55:18 PM
Long-term archiving on: : Friday, December 16, 2016 - 5:22:25 PM


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  • HAL Id : hal-00735926, version 1


Alain Rakotomamonjy, Rémi Flamary, Florian Yger. Learning with infinitely many features. Machine Learning, 2012. ⟨hal-00735926⟩



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