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.Comment: ESANN 2020 proceedings, European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning. Online event, 2-4
October 2020, i6doc.com publ., ISBN 978-2-87587-074-2. Available from
http://www.i6doc.com/en
