Title :
Robust independent component analysis algorithms for projection pursuit
Author :
Thawonmas, Ruck ; Cao, Jianting
Author_Institution :
Dept. of Inf. Syst. Eng., Kochi Univ. of Technol., Japan
fDate :
6/21/1905 12:00:00 AM
Abstract :
This paper presents the derivation of batch-mode neural algorithms which seek to robustly identify interesting projections of high dimensional data. The new index for projection pursuit, is a measure of the difference between a platykurtic density and a leptokurtic density. The robustness experiment is conducted to verify the validity of the proposed index when the algorithms are applied to artificial data and commonly used benchmark “crab” data
Keywords :
data analysis; neural nets; statistical analysis; batch-mode neural algorithms; benchmark; data analysis; high dimensional data projections; leptokurtic density; platykurtic density; projection pursuit; robust independent component analysis algorithms; Biomedical measurements; Data mining; Density measurement; Independent component analysis; Information systems; Linear discriminant analysis; Neural networks; Pursuit algorithms; Robustness; Signal processing algorithms;
Conference_Titel :
Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
Conference_Location :
Tokyo
Print_ISBN :
0-7803-5731-0
DOI :
10.1109/ICSMC.1999.823350
