Work

Extensive assessment of Barnes-Hut t-SNE.

Year: 2018

Type: conference-paper

Abstract: Stochastic Neighbor Embedding (SNE) and variants are dimensionality reduction (DR) methods able to foil the curse of dimensionality to deliver outstanding experimental results. Mitigating the crowding problem, t-SNE became an extremely popular DR scheme. Its quadratic time complexity in the number of samples is nevertheless unaffordable for big data sets. This motivates its Barnes-Hut (BH) acceleration for large-scale use. Although the latter is faster by orders of magnitude, few studies quantify its DR quality with respect to t-SNE. Extensive comparisons between t-SNE and its BH version are conducted using neighborhood preservation-based criteria. Both methods perform very similarly, suggesting the BH scheme superiority thanks to its reduced time complexity.

Source: Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B))

Authors: Cyril de Bodt, Dounia Mulders, Michel Verleysen, John A. Lee

Institution: UCLouvain

Language: English

Topic: Face and Expression Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2884323182

FWCI: 0.294

Cites: 16

Cited by: 6

Related to: 10