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The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm has become in recent years one of the most used and insightful techniques for the exploratory data analysis of high-dimensional data.
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Visualizing data using t-SNE
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Information retrieval perspective to nonlinear dimensionality reduction for data visualization
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Efficient k-nearest neighbor graph construction for generic similarity measures
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viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia
E.-a. D. Amir, K. L. Davis, M. D. Tadmor, E. F. Simonds, J. H. Levine, S. C. Bendall, D. K. Shenfeld, S. Krishnaswamy, G. P. Nolan, and D. Pe’er · 2013
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High-dimensional analysis of the murine myeloid cell system
B. Becher, A. Schlitzer, J. Chen, F. Mair, H. R. Sumatoh, K. W. W. Teng, D. Low, C. Ruedl, P. Riccardi-Castagnoli, and M. Poidinger · 2014
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Scalable nearest neighbor algorithms for high dimensional data
M. Muja and D. Lowe · 2014
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Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. Manning · 2014
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Accelerating t-sne using tree-based algorithms
Visualizing large-scale and high-dimensional data
J. Tang, J. Liu, M. Zhang, and Q. Mei · 2016
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Cyteguide: Visual guidance for hierarchical single-cell analysis
T. Höllt, N. Pezzotti, V. van Unen, F. Koning, B. P. Lelieveldt, and A. Vilanova · 2017
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High dimensional inspector, 2017
N. Pezzotti · 2017
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Interactive visual analysis of mass cytometry data by hierarchical stochastic neighbor embedding reveals rare cell types
V. van Unen, T. Hollt, N. Pezzotti, N. Li, M. J. T. Reinders, E. Eisemann, A. Vilanova, F. Koning, and B. P. F. Lelieveldt · 2017
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Deep learning
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