public class NormalizingKernel extends NestedKernel
This is equivalent to making the locations in feature space of the nested
kernel unit vectors lying on a unit sphere. The dot product in feature space
then becomes just
cos theta rather than
||a|| * ||b|| * cos theta as both lengths are 1. The length of
a in the feature space of kernel k is
sqrt( k(a, a) ), so that
the normalizing kernel ends up calculating
k(a, b) / sqrt( k(a, a) * k(b, b) ).
As the values of k(x, x) are required repeatedly, it may be worth making the nested kernel a DiagonalCachingKernel.
|Constructor and Description|
|Modifier and Type||Method and Description|
Return the dot product of two vectors in an arbitrary feature space.
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