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
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;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.833473
