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t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for dimensionality reduction and visualization that has become widely popular in recent years.
A hierarchical O(N log N) force-calculation algorithm
Barnes, J. and Hut, P. (1986) · 1986
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Data structures and algorithms for nearest neighbor search in general metric spaces
Yianilos, P. N. (1993) · 1993
Earlier work this paper cites.
Visualizing data using t-SNE
van der Maaten, L. and Hinton, G. (2008) · 2008
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Accelerating t-SNE using tree-based algorithms
van der Maaten, L. (2014) · 2014
Earlier work this paper cites.
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · 2015
Earlier work this paper cites.
Randomized algorithms for low-rank matrix factorizations: sharp performance bounds
Witten, R. and Candes, E. (2015) · 2015
Cited alongside, same era.
Transciptional profiling of 1.3 million brain cells with the chromium single cell 3’ solution
10X Genomics (2016) · 2016
Cited alongside, same era.
How to use t-SNE effectively
Wattenberg, M., Viégas, F., and Johnson, I. (2016) · 2016
Cited alongside, same era.
Annoy: Approximate nearest neighbors in c++/python optimized for memory usage and loading/saving to disk
Bernhardsson, E. (2017) · 2017
Cited alongside, same era.
Algorithm 971: an implementation of a randomized algorithm for principal component analysis
Li, H., Linderman, G. C., Szlam, A., Stanton, K. P., Kluger, Y., and Tygert, M. (2017) · 2017
Cited alongside, same era.
An algorithm for the principal component analysis of large data sets
Halko, N., Martinsson, P.-G., Shkolnisky, Y., and Tygert, M. (2011a)
Randomized near neighbor graphs, giant components, and applications in data science
Linderman, G. C., Mishne, G., Kluger, Y., and Steinerberger, S. (2017) · 2017
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Clustering with t-SNE, provably
Linderman, G. C. and Steinerberger, S. (2017) · 2017
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Data-driven tree transforms and metrics
Mishne, G., Talmon, R., Cohen, I., Coifman, R. R., and Kluger, Y. (2017) · 2017
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R: A Language and Environment for Statistical Computing
R Core Team (2017) · 2017
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Cited in the paper.
Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
Halko, N., Martinsson, P.-G., and Tropp, J. A. (2011b)
Cited in the paper.