An Efficient Dual-Hierarchy t-SNE Minimization

An Efficient Dual-Hierarchy t-SNE Minimization

van de Ruit, M. (TU Delft Computer Graphics and Visualisation)
Billeter, M.J. (University of Leeds)
Eisemann, E. (TU Delft Computer Graphics and Visualisation)

2021

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.

GPGPU
High dimensional data
dimensionality reduction
dual-hierarchy
parallel data structures

http://resolver.tudelft.nl/uuid:f4ee8e43-fe5e-4711-a1be-d9de4a88ef6d

https://doi.org/10.1109/TVCG.2021.3114817

1941-0506

IEEE Transactions on Visualization and Computer Graphics, 28 (1), 614-622

Institutional Repository

journal article

© 2021 M. van de Ruit, M.J. Billeter, E. Eisemann