Laplacian-based Cluster-Contractive t-SNE for High Dimensional Data Visualization. (arXiv:2207.12214v2 [cs.LG] UPDATED)

Dimensionality reduction techniques aim at representing high-dimensional data
in low-dimensional spaces to extract hidden and useful information or
facilitate visual understanding and interpretation of the data. However, few of
them take into consideration the potential cluster information contained
implicitly in the high-dimensional data. In this paper, we propose LaptSNE, a
new graph-layout nonlinear dimensionality reduction method based on t-SNE, one
of the best techniques for visualizing high-dimensional data as 2D scatter
plots. Specifically, LaptSNE leverages the eigenvalue information of the …

arxiv cluster data data visualization visualization