所以,总的来说,t-SNE是一个很好的可视化工具,但是其不适合做训练与测试的任务。就是就拿手写数字识别任务来是哦,如果你用t-SNE来进行降维然后再用一个分类算法比如svm或者随机森林来进行分类,其实效果是不好的。这也是无监督算法的一个毛病就是,你已经把他聚类成了10个簇,但其实这时候不知道哪个簇是哪一类,能...
roughly, that points which are close to one another in the high-dimensional data set will tend to be close to one another in the chart. t-SNE also produces beautiful looking visualizations
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roughly, that points which are close to one another in the high-dimensional data set will tend to be close to one another in the chart. t-SNE also produces beautiful looking visualizations
t-SNE visualizations The first data set I am going to use contains the classification of 10 different types of leaf based on their physical characteristics. In this case t-SNE takes as input 14 numeric variables. These include the elongation and aspect ratio of the leaves. The following chart...