Name
Abstract | ||
|---|---|---|
This paper builds on earlier work in [1] on metrics for power spectral densities (PSD) of multivariable time-series. We present an approach to quantify dissimilarities aimed at optimal prediction and smoothing. Divergence measures are constructed based on the degradation of prediction-error and smoothing-error variances. These induce Riemannian metrics which generalize earlier results for scalar PSD's. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CDC.2012.6426046 | CDC |
Keywords | Field | DocType |
prediction-error variances,smoothing-error variances,multivariate power spectra,riemannian metrics,optimal prediction,divergence measures,psd,multivariable time-series,power spectral densities,time series | Mathematical optimization,Multivariable calculus,Divergence,Multivariate statistics,Scalar (physics),Spectral line,Smoothing,Mathematics | Conference |
ISSN | ISBN | Citations |
0743-1546 E-ISBN : 978-1-4673-2064-1 | 978-1-4673-2064-1 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lipeng Ning | 1 | 125 | 15.11 |
| Xianhua Jiang | 2 | 55 | 6.25 |
| Tryphon T. Georgiou | 3 | 211 | 36.71 |