Mercer kernel-based clustering in feature space
Girolami, M.
Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol.;
This paper appears in: Neural Networks, IEEE Transactions on
Publication Date: May 2002
Volume: 13,
Issue: 3
On page(s): 780-784
ISSN: 1045-9227
References Cited: 11
CODEN: ITNNEP
INSPEC Accession Number: 7298614
Digital Object Identifier: 10.1109/TNN.2002.1000150
Current Version Published: 2002-08-07
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
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