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The paper presents an O(N log N)-implementation of t-SNE -- an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots and that normally runs in O(N^2).
Organization and maintenance of large ordered indexes
R. Bayer and E. McCreight · 1972
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Rapid solution of integral equations of classic potential theory
V. Rokhlin · 1985
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A hierarchical O(N log N) force-calculation algorithm
J. Barnes and P. Hut · 1986
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A fast algorithm for particle simulations
L. Greengard and V. Rokhlin · 1987
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Increased rates of convergence through learning rate adaptation
R.A. Jacobs · 1988
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A parallel hashed octtree N-body algorithm
M.S. Warren and J.K. Salmon · 1993
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Data structures and algorithms for nearest neighbor search in general metric spaces
P.N. Yianilos · 1993
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Skeletons from the treecode closet
J.K. Salmon and M.S. Warren · 1994
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Approximate nearest neighbors: Towards removing the curse of dimensionality
P. Indyk and R. Motwani · 1998
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Nonlinear dimensionality reduction by Locally Linear Embedding
S.T. Roweis and L.K. Saul · 2000
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A global geometric framework for nonlinear dimensionality reduction
J.B. Tenenbaum, V. de Silva, and J.C. Langford · 2000
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N-body problems in statistical learning
A.G. Gray and A.W. Moore · 2001
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Learning distributed representations of concepts using linear relational embedding
A. Paccanaro and G.E. Hinton · 2001
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Rapid evaluation of multiple density models
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Improved fast Gauss transform and efficient kernel density estimation
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80 million tiny images: A large dataset for non-parametric object and scene recognition
A. Torralba, R. Fergus, and W.T. Freeman · 2008
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Visualizing data using t-SNE
L.J.P. van der Maaten and G.E. Hinton · 2008
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Spectral hashing
Y. Weiss, A. Torralba, and R. Fergus · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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The elastic embedding algorithm for dimensionality reduction
M.Á. Carreira-Perpiñán · 2010
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A tour through the visualization zoo
J. Heer, M. Bostock, and V. Ogievetsky · 2010
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Cover trees for nearest neighbor
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Fast Krylov methods for N-body learning
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Information retrieval perspective to nonlinear dimensionality reduction for data visualization
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Learning structured embeddings of knowledge bases
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Spectral dimensionality reduction via maximum entropy
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Partial-Hessian strategies for fast learning of nonlinear embeddings
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