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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Besson, O. Dobigeon, N. Tourneret, J.-Y. |
| Copyright Year | 1991 |
| Abstract | In numerous applications, it is required to estimate the principal subspace of the data, possibly from a very limited number of samples. Additionally, it often occurs that some rough knowledge about this subspace is available and could be used to improve subspace estimation accuracy in this case. This is the problem we address herein and, in order to solve it, a Bayesian approach is proposed. The main idea consists of using the CS decomposition of the semi-orthogonal matrix whose columns span the subspace of interest. This parametrization is intuitively appealing and allows for non informative prior distributions of the matrices involved in the CS decomposition and very mild assumptions about the angles between the actual subspace and the prior subspace. The posterior distributions are derived and a Gibbs sampling scheme is presented to obtain the minimum mean-square distance estimator of the subspace of interest. Numerical simulations and an application to real hyperspectral data assess the validity and the performances of the estimator. |
| Sponsorship | IEEE Signal Processing Society |
| Starting Page | 4210 |
| Ending Page | 4218 |
| Page Count | 9 |
| File Size | 2163089 |
| File Format | |
| ISSN | 1053587X |
| Volume Number | 60 |
| Issue Number | 8 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-08-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Signal to noise ratio Covariance matrix Estimation Matrix decomposition Hyperspectral imaging Manifolds Proposals subspace estimation Bayesian inference CS decomposition minimum mean-square distance estimation simulation method Stiefel manifold |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Electrical and Electronic Engineering |
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