Name
Title | ||
|---|---|---|
Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing. |
Abstract | ||
|---|---|---|
Conventional partial correlation coefficients (PCC) were extended to the non-Gaussian case, in particular to independent component analysis (ICA) models of the observed multivariate samples. Thus, the usual methods that define the pairwise connections of a graph from the precision matrix were correspondingly extended. The basic concept involved replacing the implicit linear estimation of conventional PCC with a nonlinear estimation (conditional mean) assuming ICA. Thus, it is better eliminated the correlation between a given pair of nodes induced by the rest of nodes, and hence the specific connectivity weights can be better estimated. Some synthetic and real data examples illustrate the approach in a graph signal processing context. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/e21010022 | ENTROPY |
Keywords | Field | DocType |
partial correlation,independent component analysis,graph signal processing | Pairwise comparison,Mathematical optimization,Partial correlation,Nonlinear system,Matrix (mathematics),Conditional expectation,Algorithm,Correlation,Gaussian,Independent component analysis,Mathematics | Journal |
Volume | Issue | ISSN |
21 | 1 | 1099-4300 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jordi Belda | 1 | 2 | 1.78 |
| L. Vergara | 2 | 68 | 18.45 |
| Gonzalo Safont | 3 | 54 | 12.55 |
| Addisson Salazar | 4 | 121 | 23.46 |