We consider the problem of joint estimation of structured inverse covariance matrices. We assume the structure is unknown and perform the estimation using groups of measurements coming from populations with different covariances. Given that the inverse covariances 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 estimation of the inverse covariances. We propose a novel optimization algorithm discovering and exploring the underlying structure and provide its efficient implementation. Numerical simulations are presented to illustrate the performance benefits of the proposed algorithm.
|Original language||American English|
|Title of host publication||2015 23rd European Signal Processing Conference, EUSIPCO 2015|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - 22 Dec 2015|
|Event||23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France|
Duration: 31 Aug 2015 → 4 Sep 2015
|Name||2015 23rd European Signal Processing Conference, EUSIPCO 2015|
|Conference||23rd European Signal Processing Conference, EUSIPCO 2015|
|Period||31/08/15 → 4/09/15|
Bibliographical notePublisher Copyright:
© 2015 EURASIP.
- Structured inverse covariance estimation
- graphical models
- joint inverse covariance estimation