Name
Title | ||
|---|---|---|
Learning the solution sparsity of an ill-posed linear inverse problem with the Variational Garrote |
Abstract | ||
|---|---|---|
The Variational Garrote is a promising new approach for sparse solutions of ill-posed linear inverse problems (Kappen and Gomez, 2012). We reformulate the prior of the Variational Garrote to follow a simple Binomial law and assign a Beta hyper-prior on the parameter. With the new prior the Variational Garrote, we show, has a wide range of parameter values for which it at the same time provides low test error and high retrieval of the true feature locations. Furthermore, the new form of the prior and associated hyper-prior leads to a simple update rule in a Bayesian variational inference scheme for its hyperparameter. As a second contribution we provide evidence that the new procedure can improve on cross-validation of the parameters and we find that the new formulation of the prior outperforms the original formulation when both are cross-validated to determine hyperparameters. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MLSP.2013.6661919 | Machine Learning for Signal Processing |
Keywords | Field | DocType |
Bayes methods,binomial distribution,inverse problems,variational techniques,Bayesian variational inference scheme,Beta hyper-prior,hyperparameter,ill-posed linear inverse problem,parameter cross-validation,parameter values,simple binomial law,solution sparsity learning,true feature locations,update rule,variational Garrote,Ill-posed inverse problem,Variational Garrote,linear regression | Binomial distribution,Mathematical optimization,Well-posed problem,Hyperparameter,Computer science,Inference,Binomial,Inverse problem,Bayesian probability | Conference |
ISSN | Citations | PageRank |
1551-2541 | 0 | 0.34 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Riis Andersen | 1 | 0 | 0.34 |
| Sofie Therese Hansen | 2 | 9 | 3.93 |
| Lars Kai Hansen | 3 | 2776 | 341.03 |