|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM201312468 |
| 003 |
DE-627 |
| 005 |
20231223222211.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231223s2011 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2010.2071390
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0671.xml
|
| 035 |
|
|
|a (DE-627)NLM201312468
|
| 035 |
|
|
|a (NLM)20813641
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Cao, Guangzhi
|e verfasserin
|4 aut
|
| 245 |
1 |
4 |
|a The sparse matrix transform for covariance estimation and analysis of high dimensional signals
|
| 264 |
|
1 |
|c 2011
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 27.05.2011
|
| 500 |
|
|
|a Date Revised 17.02.2011
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a Covariance estimation for high dimensional signals is a classically difficult problem in statistical signal analysis and machine learning. In this paper, we propose a maximum likelihood (ML) approach to covariance estimation, which employs a novel non-linear sparsity constraint. More specifically, the covariance is constrained to have an eigen decomposition which can be represented as a sparse matrix transform (SMT). The SMT is formed by a product of pairwise coordinate rotations known as Givens rotations. Using this framework, the covariance can be efficiently estimated using greedy optimization of the log-likelihood function, and the number of Givens rotations can be efficiently computed using a cross-validation procedure. The resulting estimator is generally positive definite and well-conditioned, even when the sample size is limited. Experiments on a combination of simulated data, standard hyperspectral data, and face image sets show that the SMT-based covariance estimates are consistently more accurate than both traditional shrinkage estimates and recently proposed graphical lasso estimates for a variety of different classes and sample sizes. An important property of the new covariance estimate is that it naturally yields a fast implementation of the estimated eigen-transformation using the SMT representation. In fact, the SMT can be viewed as a generalization of the classical fast Fourier transform (FFT) in that it uses "butterflies" to represent an orthonormal transform. However, unlike the FFT, the SMT can be used for fast eigen-signal analysis of general non-stationary signals
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
|
| 700 |
1 |
|
|a Bachega, Leonardo R
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Bouman, Charles A
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 20(2011), 3 vom: 15. März, Seite 625-40
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:20
|g year:2011
|g number:3
|g day:15
|g month:03
|g pages:625-40
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2010.2071390
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 20
|j 2011
|e 3
|b 15
|c 03
|h 625-40
|