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Neighbor embeddings are a family of methods for visualizing complex high-dimensional datasets using $k$NN graphs.
Graph drawing by force-directed placement
Thomas M. J. Fruchterman and Edward M. Reingold · 1991
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2002
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Stochastic neighbor embedding
Geoffrey E Hinton and Sam T Roweis · 2003
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Diffusion maps
Ronald R Coifman and Stéphane Lafon · 2006
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Energy models for graph clustering
Andreas Noack · 2007
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Measuring and testing dependence by correlation of distances
Gabor Szekely, Maria Rizzo, and Nail Bakirov · 2007
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Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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Consistency of spectral clustering
Ulrike von Luxburg, Mikhail Belkin, and Olivier Bousquet · 2008
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Modularity clustering is force-directed layout
Andreas Noack · 2009
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Heavy-tailed symmetric stochastic neighbor embedding
Zhirong Yang, Irwin King, Zenglin Xu, and Erkki Oja · 2009
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The elastic embedding algorithm for dimensionality reduction
Miguel A Carreira-Perpiñán · 2010
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Hubs in space: Popular nearest neighbors in high-dimensional data
Milos Radovanovic, Alexandros Nanopoulos, and Mirjana Ivanovic · 2010
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Information retrieval perspective to nonlinear dimensionality reduction for data visualization
Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, and Samuel Kaski · 2010
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Cython: The best of both worlds
S. Behnel, R. Bradshaw, C. Citro, L. Dalcin, D.S. Seljebotn, and K. Smith · 2011
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Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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A maxent-stress model for graph layout
Emden R. Gansner, Yifan Hu, and Stephen North · 2012
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael U. Gutmann and Aapo Hyvärinen · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Scalable optimization of neighbor embedding for visualization
Zhirong Yang, Jaakko Peltonen, and Samuel Kaski · 2013
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ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software
Mathieu Jacomy, Tommaso Venturini, Sebastien Heymann, and Mathieu Bastian · 2014
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Accelerating t-SNE using tree-based algorithms
Laurens van der Maaten · 2014
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Optimization equivalence of divergences improves neighbor embedding
Zhirong Yang, Jaakko Peltonen, and Samuel Kaski · 2014
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Optimizing the information retrieval trade-off in data visualization using α \alpha -divergence
Ehsan Amid, Onur Dikmen, and Erkki Oja · 2015
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Alpha-beta divergences discover micro and macro structures in data
Karthik S Narayan, Ali Punjani, and Pieter Abbeel · 2015
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Visualizing large-scale and high-dimensional data
Jian Tang, Jingzhou Liu, Ming Zhang, and Qiaozhu Mei · 2016
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dcor: distance correlation and related E-statistics in Python
Carlos Ramos Carreño · 2017
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forceatlas2: Fastest Gephi’s ForceAtlas2 graph layout algorithm implemented for Python and NetworkX
Organoid single-cell genomic atlas uncovers human-specific features of brain development
Sabina Kanton, Michael James Boyle, Zhisong He, Malgorzata Santel, Anne Weigert, Fátima Sanchís-Calleja, Patricia Guijarro, Leila Sidow, Jonas Simon Fleck, Dingding Han, et al · 2019
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The art of using t-SNE for single-cell transcriptomics
Dmitry Kobak and Philipp Berens · 2019
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Clustering with t-SNE, provably
George C Linderman and Stefan Steinerberger · 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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Visualizing structure and transitions in high-dimensional biological data
Kevin R Moon, David van Dijk, Zheng Wang, Scott Gigante, Daniel B Burkhardt, William S Chen, Kristina Yim, Antonia van den Elzen, Matthew J Hirn, Ronald R Coifman, et al · 2019
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Bhargav Chippada · 2017
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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An analysis of the t-SNE algorithm for data visualization
Sanjeev Arora, Wei Hu, and Pravesh K Kothari · 2018
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Deep learning for classical Japanese literature
Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, and David Ha · 2018
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Stochastic neighbor embedding under F-divergences
Daniel Jiwoong Im, Nakul Verma, and Kristin Branson · 2018
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UMAP: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
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PyAMG: Algebraic multigrid solvers in Python v4.0, 2018
L. N. Olson and J. B. Schroder · 2018
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How exactly UMAP works
Nikolay Oskolkov · 2019
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openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding
Pavlin Gregor Poličar, Martin Strazar, and Blaz Zupan · 2019
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Kannada-MNIST: A new handwritten digits dataset for the Kannada language
Vinay Uday Prabhu · 2019
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Stem cell differentiation trajectories in hydra resolved at single-cell resolution
Stefan Siebert, Jeffrey A. Farrell, Jack F. Cazet, Yashodara Abeykoon, Abby S. Primack, Christine E. Schnitzler, and Celina E. Juliano · 2019
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NCVis: Noise contrastive approach for scalable visualization
Aleksandr Artemenkov and Maxim Panov · 2020
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The mutational constraint spectrum quantified from variation in 141,456 humans
Konrad J Karczewski, Laurent C Francioli, Grace Tiao, Beryl B Cummings, Jessica Alföldi, Qingbo Wang, Ryan L Collins, Kristen M Laricchia, Andrea Ganna, Daniel P Birnbaum, et al · 2020
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Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations
Dmitry Kobak, George Linderman, Stefan Steinerberger, Yuval Kluger, and Philipp Berens · 2020
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GPU-embedding of kNN-graph representing large and high-dimensional data
Bartosz Minch, Mateusz Nowak, Rafał Wcisło, and Witold Dzwinel · 2020
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A tractable latent variable model for nonlinear dimensionality reduction
Lawrence K Saul · 2020
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The emergence of transcriptional identity in somatosensory neurons
Nikhil Sharma, Kali Flaherty, Karina Lezgiyeva, Daniel E Wagner, Allon M Klein, and David D Ginty · 2020
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Lineage tracing on transcriptional landscapes links state to fate during differentiation
Caleb Weinreb, Alejo Rodriguez-Fraticelli, Fernando D Camargo, and Allon M Klein · 2020
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Minimum-distortion embedding
Akshay Agrawal, Alnur Ali, and Stephen Boyd · 2021
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On UMAP’s true loss function
Sesbastian Damrich and Fred Hamprecht · 2021
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Initialization is critical for preserving global data structure in both t-SNE and UMAP
Dmitry Kobak and George Linderman · 2021
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Parametric UMAP embeddings for representation and semisupervised learning
Tim Sainburg, Leland McInnes, and Timothy Q Gentner · 2021
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