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T-distributed stochastic neighbour embedding (t-SNE) is a widely used data visualisation technique.
Hinton, G., Roweis, S.: Stochastic neighbor embedding. In: Advances in Neural Information Processing Systems. pp. 857–864 (2003)
2003
Earlier work this paper cites.
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. Journal of Machine Learning Research 9
2008
Earlier work this paper cites.
Schmidt, B.: Stable random projection: Lightweight, general-purpose dimensionality reduction for digitized libraries. Journal of Cultural Analytics (2008)
2008
Earlier work this paper cites.
Lee, J.A., Verleysen, M.: Quality assessment of dimensionality reduction: Rank-based criteria. Neurocomputing 72
2009
Earlier work this paper cites.
van der Maaten, L.: Learning a parametric embedding by preserving local structure. In: International Conference on Artificial Intelligence and Statistics. pp. 384–391 (2009)
2009
Earlier work this paper cites.
Yang, Z., King, I., Xu, Z., Oja, E.: Heavy-tailed symmetric stochastic neighbor embedding. In: Advances in Neural Information Processing Systems. pp. 2169–2177 (2009)
2009
Earlier work this paper cites.
Amir, E.a.D., Davis, K.L., Tadmor, M.D., Simonds, E.F., Levine, J.H., Bendall, S.C., Shenfeld, D.K., Krishnaswamy, S., Nolan, G.P., Pe’er, D.: viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nature Biotechnology 31
2013
Earlier work this paper cites.
Bernhardsson, E.: Annoy. https://github.com/spotify/annoy
2013
Cited alongside, same era.
van der Maaten, L.: Accelerating t-SNE using tree-based algorithms. Journal of Machine Learning Research 15
2014
Cited alongside, same era.
Tang, J., Liu, J., Zhang, M., Mei, Q.: Visualizing large-scale and high-dimensional data. In: Proceedings of the 25th International Conference on World Wide Web. pp. 287–297. International World Wide Web Conferences Steering Committee (2016)
2016
Cited alongside, same era.
Wattenberg, M., Viégas, F., Johnson, I.: How to use t-SNE effectively. Distill 1
2016
Cited alongside, same era.
Belkina, A.C., Ciccolella, C.O., Anno, R., Spidlen, J., Halpert, R., Snyder-Cappione, J.: Automated optimal parameters for t-distributed stochastic neighbor embedding improve visualization and allow analysis of large datasets. bioRxiv (2018)
2018
Im, D.J., Verma, N., Branson, K.: Stochastic neighbor embedding under f-divergences. arXiv (2018)
2018
Later among the works it cites.
Kobak, D., Berens, P.: The art of using t-SNE for single-cell transcriptomics. bioRxiv (2018)
2018
Later among the works it cites.
McInnes, L., Healy, J., Melville, J.: UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv (2018)
2018
Later among the works it cites.
Tasic, B., Yao, Z., Graybuck, L.T., Smith, K.A., Nguyen, T.N., Bertagnolli, D., Goldy, J., Garren, E., Economo, M.N., Viswanathan, S., et al.: Shared and distinct transcriptomic cell types across neocortical areas. Nature 563
2018
Later among the works it cites.
Zeisel, A., Hochgerner, H., Lonnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Haring, M., Braun, E., Borm, L., La Manno, G., et al.: Molecular architecture of the mouse nervous system. Cell 174
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Cited alongside, same era.
Diaz-Papkovich, A., Anderson-Trocme, L., Gravel, S.: Revealing multi-scale population structure in large cohorts. bioRxiv (2018)
2018
Cited alongside, same era.
2018
Later among the works it cites.
Linderman, G.C., Rachh, M., Hoskins, J.G., Steinerberger, S., Kluger, Y.: Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nature Methods 16
2019
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