Conference item : Conference-proceeding
Preconditioning kernel matrices
- Abstract:
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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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(Version of record, pdf, 833.6KB)
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Funding
Bibliographic Details
- 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:
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2640-3498
- Source identifiers:
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664823
Item Description
- Keywords:
- Subtype:
- conference-proceeding
- Pubs id:
-
pubs:664823
- UUID:
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uuid:7de931ac-bfee-4233-b237-7ce5a8a612e4
- Local pid:
- pubs:664823
- Deposit date:
- 2016-12-09
Terms of use
- Copyright holder:
- ©Cutajar et al
- Notes:
- The final version is available online from PMLR at: http://proceedings.mlr.press/v48/
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