UMAP does not preserve global structure any better than t-SNE when using the same initialization

By Dmitry Kobak, George C. Linderman. 2019

46 citations recorded by the source corpus.

One of the most ubiquitous analysis tools employed in single-cell transcriptomics and cytometry is t-distributed stochastic neighbor embedding (t-SNE) [1], used to visualize individual cells as points on a 2D scatter plot such that similar cells are positioned close together. Recently, a related algorithm, called uniform manifold approximation and projection (UMAP) [2] has attracted substantial attention in the single-cell community. In Nature Biotechnology, Becht et al. [3] argued that UMAP is preferable to t-SNE because it better preserves the global structure of the data and is more consistent across runs. Here we show that this alleged superiority of UMAP can be entirely attributed to different choices of initialization in the implementations used by Becht et al.: t-SNE implementations by default used random initialization, while the UMAP implementation used a technique called Laplacian eigenmaps [4] to initialize the embedding. We show that UMAP with random initialization preserves global structure as poorly as t-SNE with random initialization, while t-SNE with informative initialization performs as well as UMAP with informative initialization. Hence, contrary to the claims of Becht et al., their experiments do not demonstrate any advantage of the UMAP algorithm per se, but rather warn against using random initialization.

Subfields: Statistics and Probability, Representation & Self-Supervised Learning

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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): “However, just as much recent research has discovered (Kobak and Linderman, 2019; Kobak and Berens, 2019), we found that the initialization has a much larger influence on the success rate than simply faster convergence and stability.”
A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum (2020, methodology): “This explains how UMAP benefits from its default initialization with Laplacian eigenmaps [17] and how t-SNE benefits from early exaggeration [18] (see Figure S13 for a demonstration of the importance of early exaggeration).”