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Abstract
Kernel based learning is very popular in machine learning, but many classical methods have at least quadratic runtime complexity. Random fourier features are very effective to approximate shift-invariant kernels by an explicit kernel expansion. This permits to use efficient linear models with much lower runtime complexity. As one key approach to kernelize algorithms with linear models they are successfully used in different methods. However, the number of features needed to approximate the kernel is in general still quite large with substantial memory and runtime costs. Here, we propose a simple test to identify a small set of random fourier features with linear costs, substantially reducing the number of generated features for low rank kernel matrices, while widely keeping the same representation accuracy. We also provide generalization bounds for the proposed approach.
Original language | English |
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Title of host publication | Artificial Neural Networks in Pattern Recognition - 7th IAPR TC3 Workshop, ANNPR 2016, Proceedings |
Publisher | Springer Verlag |
Pages | 42-54 |
Number of pages | 13 |
Volume | 9896 LNAI |
ISBN (Print) | 9783319461816 |
DOIs | |
Publication status | E-pub ahead of print - 9 Sept 2016 |
Event | 7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016 - Ulm, Germany Duration: 28 Sept 2016 → 30 Sept 2016 |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 9896 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016 |
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Country/Territory | Germany |
City | Ulm |
Period | 28/09/16 → 30/09/16 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
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Personalised Medicine through Learning in the Model Space
Engineering & Physical Science Research Council
1/10/13 → 31/03/17
Project: Research Councils