@article{girolami01mercer,
title = {Mercer Kernel Based Clustering in Feature Space},
author = {Mark Girolami},
journal = {I.E.E.E. Transactions on Neural Networks},
url = {/papers/upload_23060_tnnl0049_df.zip},
year = {2001},
description = {KDubiq Blueprint},
abstract = {This paper 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. },
groupsearch = {0},
keywords = {imported }
}
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