Name
Title | ||
|---|---|---|
Computationally efficient sparsity-inducing coherence spectrum estimation of complete and non-complete data sets |
Abstract | ||
|---|---|---|
The magnitude squared coherence (MSC) spectrum is an often used frequency-dependent measure for the linear dependency between two stationary processes, and the recent literature contain several contributions on how to form high-resolution data-dependent and adaptive MSC estimators, and on the efficient implementation of such estimators. In this work, we further this development with the presentation of computationally efficient implementations of the recent iterative adaptive approach (IAA) estimator, present a novel sparse learning via iterative minimization (SLIM) algorithm, discuss extensions to two-dimensional data sets, examining both the case of complete data sets and when some of the observations are missing. The algorithms further the recent development of exploiting the estimators' inherently low displacement rank of the necessary products of Toeplitz-like matrices, extending these formulations to the coherence estimation using IAA and SLIM formulations. The performance of the proposed algorithms and implementations are illustrated both with theoretical complexity measures and with numerical simulations. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.sigpro.2012.12.003 | Signal Processing |
Keywords | Field | DocType |
spectrum estimation,complete data set,computationally efficient implementation,non-complete data set,coherence estimation,computationally efficient sparsity-inducing coherence,slim formulation,adaptive msc estimator,recent iterative adaptive approach,efficient implementation,recent development,recent literature,iterative minimization | Trigonometry,Data set,Mathematical optimization,Polynomial,Matrix (mathematics),Algorithm,Toeplitz matrix,Coherence (physics),Minification,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
93 | 5 | 0165-1684 |
Citations | PageRank | References |
6 | 0.49 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Angelopoulos | 1 | 6 | 0.49 |
| G. O. Glentis | 2 | 46 | 2.93 |
| Andreas Jakobsson | 3 | 409 | 43.32 |