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Sparse Kernel Approximations for Efficient Classification and Detection

Abstract:

Efficient learning with non-linear kernels is often based on extracting features from the data that linearise the kernel. While most constructions aim at obtaining low-dimensional and dense features, in this work we explore high-dimensional and sparse ones. We give a method to compute sparse features for arbitrary kernels, re-deriving as a special case a popular map for the intersection kernel and extending it to arbitrary additive kernels. We show that bundle optimisation methods can handle ...

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Publication status:
Published

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Publisher copy:
10.1109/CVPR.2012.6247943

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
Journal:
2012 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Pages:
2320-2327
Publication date:
2012-01-01
DOI:
ISSN:
1063-6919
URN:
uuid:8d81d72b-dea9-4296-ae43-a7ff21e6a864
Source identifiers:
355074
Local pid:
pubs:355074
Language:
English

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