AbstractWe 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 data sets, 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 data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding.
The visualiza-tions produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets.
Article citationsMaaten, L.V.D., and Hinton, G. (2008) Visualizing Data Using t-SNE. Journal of Machine Learning Research, 9, 2579-2605.has been cited by the following article:.TITLE:AUTHORS:,KEYWORDS:,JOURNAL NAME:,November17,2017ABSTRACT: In this study, we aim to construct a polarity dictionary specialized for the analysis of financial policies. Based on an idea that polarity words are likely located in the secondary proximity in the dependency network, we proposed an automatic dictionary construction method using secondary LINE (Large-scale Information Network Embedding) that is a network representation learning method to quantify relationship. The results suggested the possibility of constructing a dictionary using distributed representation by LINE. We also confirmed that a distributed representation with a property different from the distributed representation by the CBOW (Continuous Bag of Word) model was acquired and analyzed the differences between the distributed representation using LINE and the distributed representation using the CBOW model.
Using t-SNE in Python Now you will apply t-SNE on an open source dataset and try to visualize the results. In addition, you will also visualize the output of PCA on the same dataset to compare it with that of t-SNE. The dataset you will be using is Fashion-MNIST dataset and can be found here (don't forget to check it out!). T-Distributed Stochastic Neighbor Embedding (t-SNE) is a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets. The technique can be implemented via Barnes-Hut approximations, allowing it to be applied on large real-world datasets.