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The Ethereal
Not-So-Random Features
October 27, 2017 ยท Declared Dead ยท ๐ International Conference on Learning Representations
"No code URL or promise found in abstract"
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Authors
Brian Bullins, Cyril Zhang, Yi Zhang
arXiv ID
1710.10230
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
22
Venue
International Conference on Learning Representations
Last Checked
5 months ago
Abstract
We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels. Our method produces a sequence of feature maps, iteratively refining the SVM margin. We provide rigorous guarantees for optimality and generalization, interpreting our algorithm as online equilibrium-finding dynamics in a certain two-player min-max game. Evaluations on synthetic and real-world datasets demonstrate scalability and consistent improvements over related random features-based methods.
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