DocumentCode
1854968
Title
Nonlinear component analysis by fuzzy clustering and multidimensional scaling methods
Author
Ikeda, Eriko ; Imaoka, Toshio ; Ichihashi, Hidetomo ; Miyoshi, Tetsuya
Author_Institution
Dept. of Ind. Eng., Osaka Prefecture Univ., Sakai, Japan
Volume
4
fYear
1999
fDate
1999
Firstpage
2539
Abstract
This paper proposes a new strategy of nonlinear component analysis for dimensionality reduction and representation of multidimensional data sets. The proposed procedure consists of two steps: one is to partition the data set into several clusters based on the local distances between two points, and the other is to project the obtained sub-manifolds on a low dimensional linear space by the multidimensional scaling methods
Keywords
data analysis; fuzzy set theory; principal component analysis; dimensionality reduction; fuzzy clustering; multidimensional data sets; multidimensional scaling; nonlinear component analysis; Algorithm design and analysis; Clustering algorithms; Educational institutions; Entropy; Helium; Industrial engineering; Multidimensional systems; Partitioning algorithms; Principal component analysis; Prototypes;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.833473
Filename
833473
Link To Document