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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

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Exeter College
Role:
Author
Journal:
International Conference on Machine Learning Journal website
Volume:
48
Pages:
2529-2538
Host title:
International Conference on Machine Learning: Proceedings of The 33rd International Conference on Machine Learning: The Proceedings of Machine Learning Research
Acceptance date:
2016-02-05
Event location:
New York City
Event start date:
2016-06-19T00:00:00Z
Event end date:
2016-12-24T00:00:00Z
EISSN:
2640-3498
Source identifiers:
664823
Keywords:
Subtype:
conference-proceeding
Pubs id:
pubs:664823
UUID:
uuid:7de931ac-bfee-4233-b237-7ce5a8a612e4
Local pid:
pubs:664823
Deposit date:
2016-12-09

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