Abstract
We consider the joint estimation of structured covariance matrices. We assume the structure is unknown and perform the estimation using heterogeneous training sets. More precisely, we are given groups of measurements coming from centered normal populations with different covariance matrices. Assuming that all these covariance matrices span a low dimensional affine subspace in the space of symmetric matrices, our aim is to determine this structure. It is then utilized to improve the covariance estimation. We provide an algorithm discovering and exploring the underlying covariance structure and analyze its error bounds. Numerical simulations are presented to illustrate the performance benefits of the proposed algorithm.
Original language | English |
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Title of host publication | 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3437-3441 |
Number of pages | 5 |
ISBN (Electronic) | 9781467369978 |
DOIs | |
State | Published - 4 Aug 2015 |
Event | 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia Duration: 19 Apr 2014 → 24 Apr 2014 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2015-August |
ISSN (Print) | 1520-6149 |
Conference
Conference | 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 |
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Country/Territory | Australia |
City | Brisbane |
Period | 19/04/14 → 24/04/14 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- Structured covariance estimation
- joint covariance estimation