  The t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm is a
ubiquitously employed dimensionality reduction (DR) method. Its non-parametric
nature and impressive efficacy motivated its parametric extension. It is
however bounded to a user-defined perplexity parameter, restricting its DR
quality compared to recently developed multi-scale perplexity-free approaches.
This paper hence proposes a multi-scale parametric t-SNE scheme, relieved from
the perplexity tuning and with a deep neural network implementing the mapping.
It produces reliable embeddings with out-of-sample extensions, competitive with
the best perplexity adjustments in terms of neighborhood preservation on
multiple data sets.
