Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding

By George C. Linderman, M. Rachh, Jeremy G. Hoskins, Stefan Steinerberger, Yuval Kluger. 2017

102 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

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Citations in the corpus

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction (2018, methodology): “3 Scaling with the Number of Samples For dataset size performance comparisons we chose to compare UMAP with FItSNE [30], a version of t-SNE that uses approximate nearest neighbor search and a Fourier interpolation optimisation approach; MulticoreTSNE [48], which we believe to be the fastest extant i”
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge (2018, background): “DarkSight also scales better than the faster O ( N log N ) variants of t-SNE (van der Maaten, 2014; Linderman et al., 2017).”
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization (2020, methodology): “For most of the modern t-SNE implementations (for example, van der Maaten, 2014; Linderman et al., 2017; Belkina et al., 2019), pij for most pairs is set to 0 within the code.”
Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets (2019, methodology): “Therefore, we expect opt-SNE to be applicable for existing or future adaptations of t-SNE even if alternative methods of computation are utilized 37,38 provided that they retain the core principles of t-SNE embedding.”
The art of using t-SNE for single-cell transcriptomics (2018, methodology): “34 suggested to use late exaggeration: using some α > 1 for some number of last iterations.”
SOM-based embedding improves efficiency of high-dimensional cytometry data analysis (2019, methodology): “For example, the widely used tSNE [24] has formed the basis 30 for faster ASNE [17] and HSNE [16], and was further improved in OptSNE and FItSNE [3, 14] and accelerated using GPU by Chan et al.”
GPU accelerated t-distributed stochastic neighbor embedding (2019, methodology): “In our experiments, we follow [37], and choose an interpolation grid of 150x150 equispaced points in the lower-dimensional space for the repulsive force approximation.”
A high-bias, low-variance introduction to Machine Learning for physicists (2018, unknown): “We used an open-source implementation to produce the results (Linderman et al., 2017), see https://github.”