Visualizing Data using t-SNE
Visualizing Data using t-SNE
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DOI：
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发表时间：
2008
影响因子：
6
通讯作者：
L. Maaten;Geoffrey E. Hinton
中科院分区：
文献类型：
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作者：
L. Maaten;Geoffrey E. Hinton
我们提出了一种称为“ T-SNE”的新技术，该技术通过在两个或三维图中给出一个位置来可视化高维数据。通过减少地图中心的人群点的趋势，可以更容易地进行优化，并且可以在T-SNE中降低趋势。为了可视化非常大的数据集的结构，我们展示了T-SNE如何在邻里图上使用随机步行，以允许所有数据的隐式结构影响显示数据的子集的方式。 T-SNE在多种数据集上，并将其与许多其他非参数可视化技术（包括Sammon映射，ISOMAP和局部线性嵌入）进行比较。在几乎所有数据集上。
We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. 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. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.