The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Analysis of Sparse Bayesian Learning
Abstract
The recent introduction of the `relevance vector machine' has eectively demonstrated how sparsity may be obtained in generalised linear models within a Bayesian framework. Using a particular form of Gaussian parameter prior, `learning' is the maximisation, with respect to hyperparameters, of the marginal likelihood of the data. This paper studies the properties of that objective function, and demonstrates that conditioned on an individual hyperparameter, the marginal likelihood has a unique maximum which is computable in closed form. It is further shown that if a derived `sparsity criterion' is satis ed, this maximum is exactly equivalent to `pruning' the corresponding parameter from the model.
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Cites
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (1998) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827596304010 · Cited by 6,845
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.415 · Cited by 4,388
Tipping, M. E. (1999) · Cited by 912
Self-citationVariational Relevance Vector Machines
Bishop, C., & Tipping, M. E. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3838 · Cited by 352
Self-citationLeast Absolute Shrinkage is Equivalent to Quadratic Penalization
Grandvalet, Y. (1998) · Perspectives in neural computing · doi:10.1007/978-1-4471-1599-1_27 · Cited by 104
Cited by
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
“However, careful analysis of the RVM marginal likelihood by Faul and Tipping [2002] showed that one can carry out optimization w.”
Ji, S., Xue, Y., & Carin, L. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.914345 · Cited by 2,406
Sparse Bayesian Learning for Basis Selection
Wipf, D. P., & Rao, B. D. (2004) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2004.831016 · Cited by 1,559
Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning
Zhang, Z., & Rao, B. D. (2011) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2011.2159773 · Cited by 868
BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease
Gaser, C., Franke, K., Klöppel, S., Koutsouleris, N., Sauer, H., & Initiative, A. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067346 · Cited by 538
Metadata
- Authors
- Faul, A. C., & Tipping, M. E.
- Venue
- Advances in Neural Information Processing Systems 14
- Publisher
- The MIT Press
- Published
- 2002-11-08
- Pages
- 383-390
- OpenAlex
- W2115606304
- Cited by
- 287
- References
- 6
- FWCI
- 10.65
- Type
- book chapter
- Language
- en
- Open access
- closed
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