Automatic Selection of t-SNE Perplexity

By Yanshuai Cao, Luyu Wang. 2017

48 citations recorded by the source corpus.

t-Distributed Stochastic Neighbor Embedding (t-SNE) is one of the most widely used dimensionality reduction methods for data visualization, but it has a perplexity hyperparameter that requires manual selection. In practice, proper tuning of t-SNE perplexity requires users to understand the inner working of the method as well as to have hands-on experience. We propose a model selection objective for t-SNE perplexity that requires negligible extra computation beyond that of the t-SNE itself. We empirically validate that the perplexity settings found by our approach are consistent with preferences elicited from human experts across a number of datasets. The similarities of our approach to Bayesian information criteria (BIC) and minimum description length (MDL) are also analyzed.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization (2020, background): “In fact, several papers have been published that navigate the challenges of tuning parameters and applying these methods in practice (see Wattenberg et al., 2016; Cao and Wang, 2017; Nguyen and Holmes, 2019; Belkina et al., 2019).”
Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets (2019, background): “…end of the suggested range for ef ﬁ cient clustering, as it is often advised to scale the number of nearest neighbors to the average cluster size 45; however, if computationally feasible, higher perplexity values might facilitate feature preservation for markers whose expression is not…”
Ten quick tips for effective dimensionality reduction (2019, unknown): “To assist practitioners, we also include in Table 2 a list of stable implementations of methods discussed in this article.”
Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda (2020, methodology): “Hence, to validate our topic modeling further, we used t-distributed stochastic neighbor embedding (t-SNE), which is a machine learning algorithm for the visualization of outcomes (Cao & Wang, 2017; Schubert et al., 2017).”