Visualizing Data using t-SNE
Citations
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38,208 citations
23,074 citations
15,696 citations
11,201 citations
Cites background or methods from "Visualizing Data using t-SNE"
...…a non-parametric approach, based on a training set nearest neighbor graph (Schölkopf et al., 1998; Roweis and Saul, 2000; Tenenbaum et al., 2000; Brand, 2003; Belkin and Niyogi, 2003; Donoho and Grimes, 2003; Weinberger and Saul, 2004; Hinton and Roweis, 2003; van der Maaten and Hinton, 2008)....
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...Corruptions considered in Vincent et al. (2010) include additive isotropic Gaussian noise, salt and pepper noise for gray-scale images, and masking noise (salt or pepper only)....
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...17 low-dimensional embedding coordinate “parameters” for each training point, these coordinates are obtained through an explicitly parametrized function, as with the parametric variant (van der Maaten, 2009) of t-SNE (van der Maaten and Hinton, 2008)....
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...…controlled number of free parameters in such parametric methods, compared to their pure non-parametric counterparts, forces models to generalize the manifold shape non-locally (Bengio et al., 2006b), which can translate into better features and final performance (van der Maaten and Hinton, 2008)....
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10,584 citations
Cites methods from "Visualizing Data using t-SNE"
...The layout of the latter is based on a t-SNE-visualization of the network (61) and can be zoomed and panned interactively....
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References
16,717 citations
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"Visualizing Data using t-SNE" refers background in this paper
..., 2004), (6) Locally Linear Embedding (LLE; Roweis and Saul, 2000), and (7) Laplacian Eigenmaps (Belkin and Niyogi, 2002)....
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...We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding....
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...…(1997)), (3) Stochastic Neighbor Embedding (SNE; Hinton and Roweis (2002)), (4) Isomap (Tenenbaum et al., 2000), (5) Maximum Variance Unfolding (MVU; Weinberger et al. (2004)), (6) Locally Linear Embedding (LLE; Roweis and Saul (2000)), and (7) Laplacian Eigenmaps (Belkin and Niyogi, 2002)....
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...In particular, we mention the following seven techniques: (1) Sammon mapping (Sammon, 1969), (2) curvilinear components analysis (CCA; Demartines and Hérault (1997)), (3) Stochastic Neighbor Embedding (SNE; Hinton and Roweis (2002)), (4) Isomap (Tenenbaum et al., 2000), (5) Maximum Variance Unfolding (MVU; Weinberger et al. (2004)), (6) Locally Linear Embedding (LLE; Roweis and Saul (2000)), and (7) Laplacian Eigenmaps (Belkin and Niyogi, 2002)....
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13,652 citations
"Visualizing Data using t-SNE" refers background or methods in this paper
...…curvilinear components analysis (CCA; Demartines and Hérault (1997)), (3) Stochastic Neighbor Embedding (SNE; Hinton and Roweis (2002)), (4) Isomap (Tenenbaum et al., 2000), (5) Maximum Variance Unfolding (MVU; Weinberger et al. (2004)), (6) Locally Linear Embedding (LLE; Roweis and Saul (2000)),…...
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...In particular, we mention the following seven techniques: (1) Sammon mapping (Sammon, 1969), (2) curvilinear components analysis (CCA; Demartines and Hérault (1997)), (3) Stochastic Neighbor Embedding (SNE; Hinton and Roweis (2002)), (4) Isomap (Tenenbaum et al., 2000), (5) Maximum Variance Unfolding (MVU; Weinberger et al....
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9,050 citations
"Visualizing Data using t-SNE" refers methods in this paper
...Traditional dimensionality reduction techniques such as Principal Components Analysis (PCA; Hotelling, 1933) and classical multidimensional scaling (MDS; Torgerson, 1952) are linear techniques that focus on keeping the low-dimensional representations of dissimilar datapoints far apart....
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...Traditional dimensionality reduction techniques such as Principal Components Analysis (PCA; Hotelling (1933)) and classical multidimensional scaling (MDS; Torgerson (1952)) are linear techniques that focus on keeping the low-dimensional representations of dissimilar datapoints far apart....
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8,307 citations
"Visualizing Data using t-SNE" refers background in this paper
...One should note that the linear system in Equation 35 is only nonsingular if the graph is completely connected, or if each connected component in the graph contains at least one landmark point (Biggs, 1974)....
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