  Choosing a suitable filtering function for the Mapper algorithm can be
difficult due to its arbitrariness and domain-specific requirements. Finding a
general filtering function that can be applied across domains is therefore of
interest, since it would improve the representation of manifolds in higher
dimensions. In this extended abstract, we propose that topological autoencoders
is a suitable candidate for this and report initial results strengthening this
hypothesis for one set of high-dimensional manifolds. The results indicate a
potential for an easier choice of filtering function when using the Mapper
algorithm, allowing for a more general and descriptive representation of
high-dimensional data.
