Expectation propagation in linear regression models with spike-and-slab priors
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| 024 | 7 | 0 | |a 10.1007/s10994-014-5475-7 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-014-5475-7 | ||
| 245 | 0 | 0 | |a Expectation propagation in linear regression models with spike-and-slab priors |h [Elektronische Daten] |c [José Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez] |
| 520 | 3 | |a An expectation propagation (EP) algorithm is proposed for approximate inference in linear regression models with spike-and-slab priors. This EP method is applied to regression tasks in which the number of training instances is small and the number of dimensions of the feature space is large. The problems analyzed include the reconstruction of genetic networks, the recovery of sparse signals, the prediction of user sentiment from customer-written reviews and the analysis of biscuit dough constituents from NIR spectra. The proposed EP method outperforms in most of these tasks another EP method that ignores correlations in the posterior and a variational Bayes technique for approximate inference. Additionally, the solutions generated by EP are very close to those given by Gibbs sampling, which can be taken as the gold standard but can be much more computationally expensive. In the tasks analyzed, spike-and-slab priors generally outperform other sparsifying priors, such as Laplace, Student's $$t$$ t and horseshoe priors. The key to the improved predictions with respect to Laplace and Student's $$t$$ t priors is the superior selective shrinkage capacity of the spike-and-slab prior distribution. | |
| 540 | |a The Author(s), 2014 | ||
| 690 | 7 | |a Spike-and-slab |2 nationallicence | |
| 690 | 7 | |a Linear regression |2 nationallicence | |
| 690 | 7 | |a Expectation propagation |2 nationallicence | |
| 690 | 7 | |a Selective shrinkage |2 nationallicence | |
| 700 | 1 | |a Hernández-Lobato |D José |u Department of Engineering, University of Cambridge, Trumpington st., CB2 1PZ, Cambridge, UK |4 aut | |
| 700 | 1 | |a Hernández-Lobato |D Daniel |u Computer Science Department, Universidad Autónoma de Madrid, Francisco Tomás y Valiente, 11, 28049, Madrid, Spain |4 aut | |
| 700 | 1 | |a Suárez |D Alberto |u Computer Science Department, Universidad Autónoma de Madrid, Francisco Tomás y Valiente, 11, 28049, Madrid, Spain |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 99/3(2015-06-01), 437-487 |x 0885-6125 |q 99:3<437 |1 2015 |2 99 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-014-5475-7 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-014-5475-7 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Hernández-Lobato |D José |u Department of Engineering, University of Cambridge, Trumpington st., CB2 1PZ, Cambridge, UK |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Hernández-Lobato |D Daniel |u Computer Science Department, Universidad Autónoma de Madrid, Francisco Tomás y Valiente, 11, 28049, Madrid, Spain |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Suárez |D Alberto |u Computer Science Department, Universidad Autónoma de Madrid, Francisco Tomás y Valiente, 11, 28049, Madrid, Spain |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 99/3(2015-06-01), 437-487 |x 0885-6125 |q 99:3<437 |1 2015 |2 99 |o 10994 | ||