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Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMAP have demonstrated impressive visualization performance on many real world datasets.
On lines and planes of closest fit to systems of points in space
Karl Pearson · 1901
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Multidimensional scaling: I theory and method
Warren Torgerson · 1952
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A database for handwritten text recognition research
J. J. Hull · 1994
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Parameterisation of a stochastic model for human face identification
F. S. Samaria and A. C. Harter · 1994
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Newsweeder: Learning to filter netnews
Ken Lang · 1995
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Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik · 1997
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Nonlinear dimensionality reduction by locally linear embedding
Sam T. Roweis and Lawrence K. Saul · 2000
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Cost-sensitive modeling for fraud and intrusion detection: Results from the jam project
Salvatore J. Stolfo, Wei Fan, Andreas Prodromidis, Philip K. Chan, and Wenke Lee · 2000
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A global geometric framework for nonlinear dimensionality reduction
Joshua B. Tenenbaum, Vin de Silva, and John C. Langford · 2000
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2001
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Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
David L. Donoho and Carrie Grimes · 2003
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Stochastic neighbor embedding
Geoffrey Hinton and Sam Roweis · 2003
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Visualizing similarity data with a mixture of maps
James Cook, Ilya Sutskever, Andriy Mnih, and Geoffrey Hinton · 2007
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Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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Dimensionality reduction: A comparative review
Laurens van der Maaten, Eric O. Postma, and Jaap van den Herik · 2009
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The elastic embedding algorithm for dimensionality reduction
Miguel Á. Carreira-Perpiñan · 2010
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Large scale online learning of image similarity through ranking
Gal Chechik, Varun Sharma, Uri Shalit, and Samy Bengio · 2010
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MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Hamming distance metric learning
Mohammad Norouzi, David Fleet, and Ruslan Salakhutdinov · 2012
Cited alongside, same era.
Stochastic triplet embedding
Laurens van der Maaten and Kilian Weinberger · 2012
Cited alongside, same era.
viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia
El-ad D. Amir, Kara L. Davis, Michelle D. Tadmor, Erin F. Simonds, Jacob H. Levine, Sean C. Bendall, Daniel K. Shenfeld, Smita Krishnaswamy, Garry P. Nolan, and Dana Pe’er · 2013
Cited alongside, same era.
A Survey on Metric Learning for Feature Vectors and Structured Data
Aurélien Bellet, Amaury Habrard, and Marc Sebban · 2013
Cited alongside, same era.
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding
George C. Linderman, Manas Rachh, Jeremy G. Hoskins, Stefan Steinerberger, and Yuval Kluger · 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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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Leland McInnes, John Healy, and James Melville · 2018
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Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo
Daniel E. Wagner, Caleb Weinreb, Zach M. Collins, James A. Briggs, Sean G. Megason, and Allon M. Klein · 2018
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A survey of two-dimensional graph layout techniques for information visualisation
Helen Gibson, Joe Faith, and Paul Vickers · 2013
Cited alongside, same era.
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
Cited alongside, same era.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
Cited alongside, same era.
Accelerating t-SNE using tree-based algorithms
Laurens van der Maaten · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Numba: A LLVM-based python JIT compiler
Siu Kwan Lam, Antoine Pitrou, and Stanley Seibert · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
Ehsan Amid and Manfred K. Warmuth · 2019
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Automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
Anna C. Belkina, Christopher O. Ciccolella, Rina Anno, Richard Halpert, Josef Spidlen, and Jennifer E. Snyder-Cappione · 2019
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Annoy: Approximate Nearest Neighbors in C++/Python , 2019
Erik Bernhardsson · 2019
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Understanding UMAP
Andy Coenen and Adam Pearce · 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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Ten quick tips for effective dimensionality reduction
Lan Huong Nguyen and Susan Holmes · 2019
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A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum
Jan Niklas Böhm, Philipp Berens, and Dmitry Kobak · 2020
Closest in time.
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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Github - lmcinnes/umap: Uniform manifold approximation and projection (umap)
McInnes, Leland · 2020
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Mammuthus primigenius (blumbach)
The Smithsonian Institute · 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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Initialization is critical for preserving global data structure in both t-SNE and UMAP
Dmitry Kobak and George C Linderman · 2021
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t-SNE, forceful colorings and mean field limits
Yulan Zhang and Stefan Steinerberger · 2021
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