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Healing the Relevance Vector Machine through Augmentation

2005

Conference Paper

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The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties have the unintuitive property, that emph{they get smaller the further you move away from the training cases}. We give a thorough analysis. Inspired by the analogy to non-degenerate Gaussian Processes, we suggest augmentation to solve the problem. The purpose of the resulting model, RVM*, is primarily to corroborate the theoretical and experimental analysis. Although RVM* could be used in practical applications, it is no longer a truly sparse model. Experiments show that sparsity comes at the expense of worse predictive distributions.

Author(s): Rasmussen, CE. and Candela, JQ.
Journal: Proceedings of the 22nd International Conference on Machine Learning
Pages: 689
Year: 2005
Day: 0
Editors: De Raedt, L. , S. Wrobel

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: ICML 2005
Event Place: Bonn, Germany

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{3460,
  title = {Healing the Relevance Vector Machine through Augmentation},
  author = {Rasmussen, CE. and Candela, JQ.},
  journal = {Proceedings of the 22nd International Conference on Machine Learning},
  pages = {689 },
  editors = {De Raedt, L. , S. Wrobel},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2005}
}