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Abstract:
In recent work by (Song et al., 2007), it has been proposed to perform clustering by maximizing a Hilbert-Schmidt independence criterion with respect to a predefined
cluster structure Y , by solving for the partition
matrix, II. We extend this approach here to the
case where the cluster structure Y is not fixed, but is
a quantity to be optimized; and we use an independence
criterion which has been shown to be more sensitive
at small sample sizes (the Hilbert-Schmidt Normalized
Information Criterion, or HSNIC, Fukumizu
et al., 2008). We demonstrate the use of this framework
in two scenarios. In the first, we adopt a cluster
structure selection approach in which the HSNIC is
used to select a structure from several candidates. In
the second, we consider the case where we discover
structure by directly optimizing Y.
