dc.contributor.author | Everson, Richard M. | en_GB |
dc.contributor.author | Roberts, Stephen | en_GB |
dc.date.accessioned | 2012-04-25T14:56:23Z | en_GB |
dc.date.accessioned | 2012-09-28T18:28:40Z | en_GB |
dc.date.accessioned | 2013-03-20T12:10:27Z | |
dc.date.issued | 2000-07-01 | en_GB |
dc.description.abstract | The eigenvalue spectrum of covariance matrices is of central importance to a number of data analysis techniques, Usually, the sample covariance matrix is constructed from a limited number of noisy samples, We describe a method of inferring the true eigenvalue spectrum from the sample spectrum. Results of Silverstein, which characterize the eigenvalue spectrum of the noise covariance matrix, and inequalities between the eigenvalues of Hermitian matrices are used to infer probability densities for the eigenvalues of the noise-free covariance matrix, using Bayesian inference. Posterior densities for each eigenvalue are obtained, which yield error estimates. The evidence framework gives estimates of the noise variance anal permits model order selection by estimating the rank of the covariance matrix, The method is illustrated with numerical examples. | en_GB |
dc.identifier.citation | Vol. 48 (7), pp. 2083 - 2091 | en_GB |
dc.identifier.doi | 10.1109/78.847792 | |
dc.identifier.uri | http://hdl.handle.net/10036/3833 | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.relation.replaces | http://hdl.handle.net/10036/3510 | en_GB |
dc.relation.replaces | 10036/3510 | en_GB |
dc.subject | Bayesian evidence | en_GB |
dc.subject | eigenvalue spectrum | en_GB |
dc.subject | model order selection | en_GB |
dc.subject | sample covariance | en_GB |
dc.title | Inferring the eigenvalues of covariance matrices from limited, noisy data | en_GB |
dc.date.available | 2012-04-25T14:56:23Z | en_GB |
dc.date.available | 2012-09-28T18:28:40Z | en_GB |
dc.date.available | 2013-03-20T12:10:27Z | |
dc.identifier.issn | 1053-587X | en_GB |
dc.description | Copyright © 2000 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. | en_GB |
dc.identifier.journal | IEEE Transactions on Signal Processing | en_GB |
refterms.dateFOA | 2023-06-05T14:30:23Z | |