van de Ruit, M, Billeter, M orcid.org/0000-0003-1806-2587 and Eisemann, E (2021) An Efficient Dual-Hierarchy t-SNE Minimization. IEEE Transactions on Visualization and Computer Graphics. p. 1. ISSN 1077-2626
Abstract
t-distributed Stochastic Neighbour Embedding (t-SNE) has become a standard for exploratory data analysis, as it is capable of revealing clusters even in complex data while requiring minimal user input. While its run-time complexity limited it to small datasets in the past, recent efforts improved upon the expensive similarity computations and the previously quadratic minimization. Nevertheless, t-SNE still has high runtime and memory costs when operating on millions of points. We present a novel method for executing the t-SNE minimization. While our method overall retains a linear runtime complexity, we obtain a significant performance increase in the most expensive part of the minimization. We achieve a significant improvement without a noticeable decrease in accuracy even when targeting a 3D embedding. Our method constructs a pair of spatial hierarchies over the embedding, which are simultaneously traversed to approximate many N-body interactions at once. We demonstrate an efficient GPGPU implementation and evaluate its performance against state-of-the-art methods on a variety of datasets.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | High dimensional data , dimensionality reduction , parallel data structures , dual-hierarchy , GPGPU |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Nov 2021 15:53 |
Last Modified: | 02 Nov 2021 09:43 |
Status: | Published online |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/tvcg.2021.3114817 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179727 |