Modern nonlinear dimensionality reduction (DR) techniques project

high dimensional data to low dimensions for their visual inspection.

Provided the intrinsic data dimensionality is larger than two, DR nec-

essarily faces information loss and the problem becomes ill-posed. Dis-

criminative dimensionality reduction (DiDi) offers one intuitive way

to reduce this ambiguity: it allows a practitioner to identify what is

relevant and what should be regarded as noise by means of intuitive

auxiliary information such as class labels. One powerful DiDi method

relies on a change of the data metric based on the Fisher information.

This technique has been presented for vectorial data so far. The aim

of this contribution is to extend the technique to more general data

structures which are characterised in terms of pairwise similarities only

by means of a kernelisation. We demonstrate that a computation of

the Fisher metric is possible in kernel space, and that it can efficiently

be integrated into modern DR technologies such as t-SNE or faster

Barnes-Hut-SNE. We demonstrate the performance of the approach

in a variety of benchmarks.