Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond

The class of random features is one of the most popular techniques to speed up kernel methods in large-scale problems. Related works have been recognized by the NeurIPS Test-of-Time award in 2017 and the ICML Best Paper Finalist in 2019. The body of work on random features has grown rapidly, and hen...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 44; H. 10; S. 7128 - 7148
Hauptverfasser:	Liu, Fanghui, Huang, Xiaolin, Chen, Yudong, Suykens, Johan A. K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Approximation Approximation algorithms Artificial neural networks Empirical analysis generalization properties Kernel kernel approximation Kernels Loss measurement Machine learning Mathematical analysis over-parameterized models Prediction algorithms Random features Risk management Scalability Taxonomy
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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