The role of multiple, linear-projection based visualization techniques in RBF-based classification of high dimensional data

Author

Agogino, A. ; Ghosh, J. ; Perantonis, S.J. ; Virvilis, V. ; Petridis, S. ; Lisboa, P.J.G.

Author_Institution

Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA

Volume

3

fYear

2000

fDate

2000

Firstpage

47

Abstract

The paper presents a method for the 3D visualization of the structure of radial basis function networks using traditional and novel methods of dimensionality reduction. This method allows the visualization of basis function characteristics (centers and widths) along with second level weights. To facilitate the interpretation of a wide variety of high dimensional problems, several forms of projections into 20 or 30 spaces can be used interactively. The traditional methods of principal component analysis and Fisher´s linear discriminant are used as well as a novel linear projection method

Keywords

data visualisation; pattern classification; principal component analysis; radial basis function networks; 3D visualization; Fisher linear discriminant; dimensionality reduction; linear-projection based visualization; pattern classification; principal component analysis; radial basis function networks; Bonding; Data visualization; Informatics; Laboratories; Neural networks; Radial basis function networks; Telecommunication computing; Training data; Uniform resource locators; World Wide Web;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Conference_Location

Como

ISSN

1098-7576

Print_ISBN

0-7695-0619-4

Type

conf

DOI

10.1109/IJCNN.2000.861279

Filename

861279