DocumentCode :
303389
Title :
Extensions of principal component analysis for nonlinear feature extraction
Author :
Sudjianto, Agus ; Hassoun, Mohamad H. ; Wasserman, G.S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Wayne State Univ., Detroit, MI, USA
Volume :
3
fYear :
1996
fDate :
3-6 Jun 1996
Firstpage :
1433
Abstract :
One of the challenges of feature extraction is the ability to deal with complexities intrinsic to vast data sets in high-dimensional spaces. Multivariate methods are generally used to identify and eliminate unnecessary dimensions so as to encourage a more parsimonious representation of the data set while retaining the maximum information content possible. In this paper, we survey multivariate statistical approaches and introduce their neural network counterparts to perform linear and nonlinear dimensionality reduction. The usefulness of the various techniques is demonstrated using real-life data
Keywords :
dimensions; feature extraction; multilayer perceptrons; optimisation; statistical analysis; data sets; dimensionality reduction; high-dimensional spaces; multivariate statistical analysis; neural network; nonlinear feature extraction; principal component analysis; Artificial neural networks; Computer aided manufacturing; Computer networks; Covariance matrix; Feature extraction; Higher order statistics; Neural networks; Principal component analysis; Statistical analysis; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1996., IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-3210-5
Type :
conf
DOI :
10.1109/ICNN.1996.549110
Filename :
549110
Link To Document :
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