Distributed Stochastic Neighbor Embedding (t-SNE) is a well-known dimensionality reduction technique used for the visualization of high-dimensional data. However, despite several improvements, t-SNE is not well-suited to handle large datasets. Indeed, for large datasets, the computation time re-quired to obtain the visualizations is still too high to incorporate it in an interactive data exploration process. Since t-SNE can be seen as an N-body problem in physics, we present a new variant of t-SNE based on a popular algorithm used to solve the N-body problem in physics called Particle-Mesh (PM). The problem is solved by first computing a potential in space and deriving from it the force exerted on each body. As the potential can be computed efficiently using Fast Fourier Transforms (FFTs), this leads to a significant speed up. The mathematical correspondence between t-SNE and PM presented in this work could also lead to other future improvements since more advanced PM algorithms have been developed in physics for decades.
|Nom||2021 International Joint Conference on Neural Networks (IJCNN)|