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Nonlinear embedding manifold learning methods provide invaluable visual insights into the structure of high-dimensional data.
Advances in Neural Information Processing Systems (NIPS) , volume 15, 2003. MIT Press, Cambridge, MA
S. Becker, S. Thrun, and K. Obermayer, editors · 2003
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Laplacian eigenmaps for dimensionality reduction and data representation
M. Belkin and P. Niyogi · 2003
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Global versus local methods in nonlinear dimensionality reduction
V. de Silva and J. B. Tenenbaum · 2003
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Stochastic neighbor embedding
G. Hinton and S. T. Roweis · 2003
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Think globally, fit locally: Unsupervised learning of low dimensional manifolds
L. K. Saul and S. T. Roweis · 2003
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Visualizing similarity data with a mixture of maps
J. Cook, I. Sutskever, A. Mnih, and G. Hinton · 2007
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Visualizing data using t t -SNE
L. J. van der Maaten and G. E. Hinton · 2008
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Quality assessment of dimensionality reduction: Rank-based criteria
J. A. Lee and M. Verleysen · 2009
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The elastic embedding algorithm for dimensionality reduction
M. Á. Carreira-Perpiñán · 2010
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Information retrieval perspective to nonlinear dimensionality reduction for data visualization
J. Venna, J. Peltonen, K. Nybo, H. Aidos, and S. Kaski · 2010
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CheckViz: Sanity check and topological clues for linear and non-linear mappings
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M. Vladymyrov and M. Á. Carreira-Perpiñán · 2012
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Distributed representations of words and phrases and their compositionality
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Scalable optimization for neighbor embedding for visualization
Z. Yang, J. Peltonen, and S. Kaski · 2013
Linear-time training of nonlinear low-dimensional embeddings
M. Vladymyrov and M. Á. Carreira-Perpiñán · 2014
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Skip-thought vectors
R. Kiros, Y. Zhu, R. R. Salakhutdinov, R. Zemel, R. Urtasun, A. Torralba, and S. Fidler · 2015
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al · 2015
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Information retrieval approach to meta-visualization
J. Peltonen and Z. Lin · 2015
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Majorization-minimization for manifold embedding
Z. Yang, J. Peltonen, and S. Kaski · 2015
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How to use t t -sne effectively
M. Wattenberg, F. Viégas, and I. Johnson · 2016
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Accelerating t-sne using tree-based algorithms
L. van der Maaten · 2014
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UMAP: Uniform manifold approximation and projection for dimension reduction
L. McInnes, J. Healy, and J. Melville · 2018
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