DocumentCode :
2952118
Title :
Estimating dependency and significance for high-dimensional data
Author :
Siracusa, Michael R. ; Tieu, Kinh ; Ihler, Alexander T. ; Fisher, John W. ; Willsky, Alan S.
Author_Institution :
Comput. Sci. & Artificial Intelligence Lab., MIT, Cambridge, MA, USA
Volume :
5
fYear :
2005
fDate :
18-23 March 2005
Abstract :
Understanding the dependency structure of a set of variables is a key component in various signal processing applications which involve data association. The simple task of detecting whether any dependency exists is particularly difficult when models of the data are unknown or difficult to characterize because of high-dimensional measurements. We review the use of nonparametric tests for characterizing dependency and how to carry out these tests with high-dimensional observations. In addition we present a method to assess the significance of the tests.
Keywords :
nonparametric statistics; optimisation; signal processing; data association; dependency structure; high-dimensional data; nonparametric tests; signal processing; Application software; Artificial intelligence; Computer science; Laboratories; Machine learning; Parametric statistics; Signal processing; Statistical analysis; Statistical distributions; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-7803-8874-7
Type :
conf
DOI :
10.1109/ICASSP.2005.1416496
Filename :
1416496
Link To Document :
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