Mercer kernel-based clustering in feature space

Author

Girolami, Mark

Author_Institution

Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Finland

Volume

13

Issue

3

fYear

2002

fDate

5/1/2002 12:00:00 AM

Firstpage

780

Lastpage

784

Abstract

The article presents a method for both the unsupervised partitioning of a sample of data and the estimation of the possible number of inherent clusters which generate the data. This work exploits the notion that performing a nonlinear data transformation into some high dimensional feature space increases the probability of the linear separability of the patterns within the transformed space and therefore simplifies the associated data structure. It is shown that the eigenvectors of a kernel matrix which defines the implicit mapping provides a means to estimate the number of clusters inherent within the data and a computationally simple iterative procedure is presented for the subsequent feature space partitioning of the data

Keywords

data analysis; eigenvalues and eigenfunctions; matrix algebra; pattern clustering; unsupervised learning; Mercer kernel-based clustering; computationally simple iterative procedure; data clustering; data generation; data partitioning; data structure; eigenvectors; feature space partitioning; high dimensional feature space; implicit mapping; inherent clusters; kernel matrix; linear separability; nonlinear data transformation; transformed space; unsupervised learning; unsupervised partitioning; Clustering methods; Costs; Councils; Data analysis; Data structures; Kernel; Libraries; Radial basis function networks; Scattering; Unsupervised learning;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2002.1000150

Filename

1000150