  Kernel PCA is a powerful feature extractor which recently has seen a
reformulation in the context of Restricted Kernel Machines (RKMs). These RKMs
allow for a representation of kernel PCA in terms of hidden and visible units
similar to Restricted Boltzmann Machines. This connection has led to insights
on how to use kernel PCA in a generative procedure, called generative kernel
PCA. In this paper, the use of generative kernel PCA for exploring latent
spaces of datasets is investigated. New points can be generated by gradually
moving in the latent space, which allows for an interpretation of the
components. Firstly, examples of this feature space exploration on three
datasets are shown with one of them leading to an interpretable representation
of ECG signals. Afterwards, the use of the tool in combination with novelty
detection is shown, where the latent space around novel patterns in the data is
explored. This helps in the interpretation of why certain points are considered
as novel.
