Title :
Mercer kernel-based clustering in feature space
Author_Institution :
Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Finland
fDate :
5/1/2002 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2002.1000150
