Name
Abstract | ||
|---|---|---|
Understanding the dependency structure of a set of variables is a key component in various signal processing applications which in- volve data association. The simple task of detecting whether any dependency exists is particularly difficult when models of the data are unknownordifficult tocharacterize because ofhigh-dimensional measurements. We review the use of nonparametric tests for char- acterizing dependency and how to carry out these tests with high- dimensional observations. In addition we present a method to as- sess the significance of the tests. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICASSP.2005.1416496 | ICASSP |
Keywords | Field | DocType |
nonparametric statistics,optimisation,signal processing,data association,dependency structure,high-dimensional data,nonparametric tests,signal processing | Signal processing,Clustering high-dimensional data,Pattern recognition,Computer science,Nonparametric statistics,Dependency structure,Data association,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
5 | 1520-6149 | 2 |
PageRank | References | Authors |
0.49 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael R. Siracusa | 1 | 13 | 1.62 |
| Kinh Tieu | 2 | 694 | 75.69 |
| Alexander T. Ihler | 3 | 1377 | 112.01 |
| Fisher, J.W. | 4 | 542 | 55.82 |
| Alan S. Willsky | 5 | 7466 | 847.01 |