Abstract
We investigate the problem of approximate inference using Expectation Propagation
(EP) for large systems under some statistical assumptions. Our approach tries
to overcome the numerical bottleneck of EP caused by the inversion of large
matrices. Assuming that the measurement matrices are realizations of specific
types of random matrix ensembles – called invariant ensembles – the EP cavity
variances have an asymptotic self-averaging property. They can be pre-computed
using specific generating functions which do not require matrix inversions. We
demonstrate the performance of our approach on a signal recovery problem of
compressed sensing and compare with standard EP.
(EP) for large systems under some statistical assumptions. Our approach tries
to overcome the numerical bottleneck of EP caused by the inversion of large
matrices. Assuming that the measurement matrices are realizations of specific
types of random matrix ensembles – called invariant ensembles – the EP cavity
variances have an asymptotic self-averaging property. They can be pre-computed
using specific generating functions which do not require matrix inversions. We
demonstrate the performance of our approach on a signal recovery problem of
compressed sensing and compare with standard EP.
| Original language | English |
|---|---|
| Publication date | 2016 |
| Number of pages | 5 |
| Publication status | Published - 2016 |
| Event | Advances in Approximate Bayesian Inference : NIPS 2016 Workshop - Duration: 9 Dec 2016 → 9 Dec 2016 http://approximateinference.org/ |
Workshop
| Workshop | Advances in Approximate Bayesian Inference |
|---|---|
| Period | 09/12/2016 → 09/12/2016 |
| Internet address |