|
20 | 20 | t-SNE are significantly better than those produced by other
|
21 | 21 | techniques on almost all of the data sets.
|
22 | 22 | authors: Laurens van der Maaten and Geoffrey Hinton
|
| 23 | + bibtex: >+ |
| 24 | + @article{van_der_maaten2008, |
| 25 | + author = {van der Maaten, Laurens and Hinton, Geoffrey}, |
| 26 | + publisher = {Société Française de Statistique}, |
| 27 | + title = {Visualizing {Data} Using {t-SNE:} A Practical Computo Example |
| 28 | + (Mock)}, |
| 29 | + journal = {Computo}, |
| 30 | + date = {2008-08-11}, |
| 31 | + url = {https://computo.sfds.asso.fr/published-paper-tsne}, |
| 32 | + issn = {2824-7795}, |
| 33 | + langid = {en}, |
| 34 | + abstract = {We present a new technique called “t-SNE” that visualizes |
| 35 | + high-dimensional data by giving each datapoint a location in a two |
| 36 | + or three-dimensional map. The technique is a variation of Stochastic |
| 37 | + Neighbor Embedding {[}@hinton:stochastic{]} that is much easier to |
| 38 | + optimize, and produces significantly better visualizations by |
| 39 | + reducing the tendency to crowd points together in the center of the |
| 40 | + map. t-SNE is better than existing techniques at creating a single |
| 41 | + map that reveals structure at many different scales. This is |
| 42 | + particularly important for high-dimensional data that lie on several |
| 43 | + different, but related, low-dimensional manifolds, such as images of |
| 44 | + objects from multiple classes seen from multiple viewpoints. For |
| 45 | + visualizing the structure of very large data sets, we show how t-SNE |
| 46 | + can use random walks on neighborhood graphs to allow the implicit |
| 47 | + structure of all the data to influence the way in which a subset of |
| 48 | + the data is displayed. We illustrate the performance of t-SNE on a |
| 49 | + wide variety of data sets and compare it with many other |
| 50 | + non-parametric visualization techniques, including Sammon mapping, |
| 51 | + Isomap, and Locally Linear Embedding. The visualization produced by |
| 52 | + t-SNE are significantly better than those produced by other |
| 53 | + techniques on almost all of the data sets.} |
| 54 | + } |
| 55 | +
|
23 | 56 | date: 2008-08-11
|
24 | 57 | description: >
|
25 | 58 | This page is a reworking of the original t-SNE article using the Computo template. It aims to help authors submitting to the journal by using some advanced formatting features. We warmly thank the authors of t-SNE and the editor of JMLR for allowing us to use their work to illustrate the Computo spirit.
|
|
29 | 62 | pdf: ''
|
30 | 63 | repo: published-paper-tsne
|
31 | 64 | title: Visualizing Data using t-SNE (mock contributon)
|
32 |
| - url: https://computo.sfds.asso.fr/published-paper-tsne |
| 65 | + url: https://computo-journal.org/published-paper-tsne |
33 | 66 | year: 2008
|
34 | 67 | - abstract': >-
|
35 | 68 | We present a new technique called “t-SNE” that visualizes
|
|
53 | 86 | t-SNE are significantly better than those produced by other
|
54 | 87 | techniques on almost all of the data sets.
|
55 | 88 | authors: Laurens van der Maaten and Geoffrey Hinton
|
| 89 | + bibtex: >+ |
| 90 | + @article{van_der_maaten2008, |
| 91 | + author = {van der Maaten, Laurens and Hinton, Geoffrey}, |
| 92 | + publisher = {French Statistical Society}, |
| 93 | + title = {Visualizing {Data} Using {t-SNE:} A Practical {Computo} |
| 94 | + Example (Mock)}, |
| 95 | + journal = {Computo}, |
| 96 | + date = {2008-08-11}, |
| 97 | + url = {https://computo-journal.org/published-paper-tsne-R}, |
| 98 | + issn = {2824-7795}, |
| 99 | + langid = {en}, |
| 100 | + abstract = {We present a new technique called “t-SNE” that visualizes |
| 101 | + high-dimensional data by giving each datapoint a location in a two |
| 102 | + or three-dimensional map. The technique is a variation of Stochastic |
| 103 | + Neighbor Embeddi{[}@hinton:stochastic{]} that is much easier to |
| 104 | + optimize, and produces significantly better visualizations by |
| 105 | + reducing the tendency to crowd points together in the center of the |
| 106 | + map. t-SNE is better than existing techniques at creating a single |
| 107 | + map that reveals structure at many different scales. This is |
| 108 | + particularly important for high-dimensional data that lie on several |
| 109 | + different, but related, low-dimensional manifolds, such as images of |
| 110 | + objects from multiple classes seen from multiple viewpoints. For |
| 111 | + visualizing the structure of very large data sets, we show how t-SNE |
| 112 | + can use random walks on neighborhood graphs to allow the implicit |
| 113 | + structure of all the data to influence the way in which a subset of |
| 114 | + the data is displayed. We illustrate the performance of t-SNE on a |
| 115 | + wide variety of data sets and compare it with many other |
| 116 | + non-parametric visualization techniques, including Sammon mapping, |
| 117 | + Isomap, and Locally Linear Embedding. The visualization produced by |
| 118 | + t-SNE are significantly better than those produced by other |
| 119 | + techniques on almost all of the data sets.} |
| 120 | + } |
| 121 | +
|
56 | 122 | date: 2008-08-11
|
57 | 123 | description: >
|
58 | 124 | This page is a reworking of the original t-SNE article using the Computo template. It aims to help authors submitting to the journal by using some advanced formatting features. We warmly thank the authors of t-SNE and the editor of JMLR for allowing us to use their work to illustrate the Computo spirit.
|
|
62 | 128 | pdf: ''
|
63 | 129 | repo: published-paper-tsne-R
|
64 | 130 | title: Visualizing Data using t-SNE (mock contributon)
|
65 |
| - url: https://computo.sfds.asso.fr/published-paper-tsne-R |
| 131 | + url: https://computo-journal.org/published-paper-tsne-R |
66 | 132 | year: 2008
|
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