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arXiv · 2010.01359

Perplexity-free Parametric t-SNE

Francesco Crecchi·Cyril de Bodt·Michel Verleysen·John A. Lee·Davide Bacciu
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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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Francesco Crecchi, Cyril de Bodt, Michel Verleysen, John A. Lee, Davide Bacciu. 2020-10-03. Perplexity-free Parametric t-SNE. https://arxiv.org/abs/2010.01359

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