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Preconditioning kernel matrices

Abstract:

The computational and storage complexity of kernel machines presents the primary barrier to their scaling to large, modern, datasets. A common way to tackle the scalability issue is to use the conjugate gradient algorithm, which relieves the constraints on both storage (the kernel matrix need not be stored) and computation (both stochastic gradients and parallelization can be used). Even so, conjugate gradient is not without its own issues: the conditioning of kernel matrices is often such th...

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Publication status:
Published
Version:
Publisher's version

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Institution:
University of Oxford
Oxford college:
Exeter College
Department:
Oxford, MPLS, Engineering Science
Role:
Author
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Grant:
SCGB#325171
SCGB#325233
Volume:
48
Pages:
2529-2538
Acceptance date:
2016-02-05
EISSN:
2640-3498
Pubs id:
pubs:664823
URN:
uri:7de931ac-bfee-4233-b237-7ce5a8a612e4
UUID:
uuid:7de931ac-bfee-4233-b237-7ce5a8a612e4
Local pid:
pubs:664823
Keywords:

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