This paper presents a kernelized version of the t-SNE algorithm, capable of
mapping high-dimensional data to a low-dimensional space while preserving the
pairwise distances between the data points in a non-Euclidean metric. This can
be achieved using a kernel trick only in the high dimensional space or in both
spaces, leading to an end-to-end kernelized version. The proposed kernelized
version of the t-SNE algorithm can offer new views on the relationships between
data points, which can improve performance and accuracy in particular
applications, such as classification problems involving kernel methods. The
differences between t-SNE and its kernelized version are illustrated for
several datasets, showing a neater clustering of points belonging to different
classes.

本文介绍了 t-SNE 算法的核化版本，能够将高维数据映射到低维空间并在非欧几里德度量下保留数据点之间的成对距离，可以通过仅在高维空间或在两个空间中使用核技巧来实现，提供了数据点之间关系的新视角，改进了包括使用核方法的分类问题的性能和准确性，并利用多个数据集阐明了 t-SNE 和其核化版本之间的区别，展示了不同类别点的更整洁的聚类。