DocumentCode
1628979
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
Volume
3
fYear
1999
fDate
6/21/1905 12:00:00 AM
Firstpage
917
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
Conference_Location
Tokyo
ISSN
1062-922X
Print_ISBN
0-7803-5731-0
Type
conf
DOI
10.1109/ICSMC.1999.823350
Filename
823350
Link To Document