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We study the empirical measure of the output of the t-distributed stochastic neighbour embedding algorithm when the initial data is given by n independent, identically distributed inputs.
Stochastic neighbor embedding
Geoffrey E Hinton and Sam Roweis · 2002
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Concentration inequalities. A nonasymptotic theory of independence
Stephane Boucheron, Gabor Lugosi, and Pascal Massart · 2013
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Accelerating t-sne using tree-based algorithms
Laurens van der Maaten · 2014
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Application of t-sne to human genetic data
W. Li, J.E. Cerise, Y. Yang, and H. Han · 2017
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Stochastic neighbor embedding separates well-separated clusters
Uri Shaham and Stefan Steinerberger · 2017
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An analysis of the t-sne algorithm for data visualization
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The art of using t-sne for single-cell transcriptomics
Dmitry Kobak and Philipp Berens · 2019
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Fast interpolation-based t-sne for improved visualization of single-cell rna-seq data
George C. Linderman, Manas Rachh, Jeremy G. Hoskins, Stefan Steinerberger, and Yuval Kluger · 2019
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Clustering with t-sne, provably
George C. Linderman and Stefan Steinerberger · 2019
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Powerful t-sne technique leading to clear separation of type-2 agn and h ii galaxies in bpt diagrams
XueGuang Zhang, Yanqiu Feng, Huan Chen, and QiRong Yuan · 2020
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An empirical evaluation of the t-sne algorithm for data visualization in structural engineering
Parisa Hajibabaee, Farhad Pourkamali-Anaraki, and Mohammad Amin Hariri-Ardebili · 2021
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Theoretical foundations of t-sne for visualizing high-dimensional clustered data
Tony Cai and Rong Ma · 2022
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Embedr: Distinguishing signal from noise in single-cell omics data
Eric M. Johnson, William Kath, and Madhav Mani · 2022
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Elements of Dimensionality Reduction and Manifold Learning
Benyamin Ghojogh, Mark Crowley, Fakhri Karray, and Ali Ghodsi · 2023
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