Perplexity-free Parametric t-SNE

• URL: http://arxiv.org/abs/2010.01359v1
• Date: Sat, 3 Oct 2020 13:47:01 GMT
• Title: Perplexity-free Parametric t-SNE
• Authors: Francesco Crecchi, Cyril de Bodt, Michel Verleysen, John A. Lee and Davide Bacciu
• Abstract summary: The t-distributed Neighbor Embedding (t-SNE) algorithm is a ubiquitously employed dimensionality reduction (DR) method. 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.
• Score: 11.970023029249083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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