Kernel t-distributed stochastic neighbor embedding
- URL: http://arxiv.org/abs/2307.07081v2
- Date: Mon, 20 Nov 2023 19:44:09 GMT
- Title: Kernel t-distributed stochastic neighbor embedding
- Authors: Denis C. Ilie-Ablachim, Bogdan Dumitrescu, Cristian Rusu
- Abstract summary: This paper presents a kernelized version of the t-SNE algorithm.
It is 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.
- Score: 6.107978190324034
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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