Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding

TL;DR

This paper introduces FIt-SNE, a significantly faster t-SNE implementation using FFT acceleration, along with methods for better cluster visualization and handling large datasets with limited memory.

Contribution

The paper presents FIt-SNE, a novel FFT-accelerated t-SNE algorithm, and introduces out-of-core PCA and late exaggeration techniques for improved scalability and interpretability.

Findings

FIt-SNE dramatically reduces t-SNE computation time.

Out-of-core PCA enables t-SNE on large datasets with limited memory.

Late exaggeration improves cluster detection in embeddings.

Abstract

t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for dimensionality reduction and visualization that has become widely popular in recent years. Efficient implementations of t-SNE are available, but they scale poorly to datasets with hundreds of thousands to millions of high dimensional data-points. We present Fast Fourier Transform-accelerated Interpolation-based t-SNE (FIt-SNE), which dramatically accelerates the computation of t-SNE. The most time-consuming step of t-SNE is a convolution that we accelerate by interpolating onto an equispaced grid and subsequently using the fast Fourier transform to perform the convolution. We also optimize the computation of input similarities in high dimensions using multi-threaded approximate nearest neighbors. We further present a modification to t-SNE called "late exaggeration," which allows for easier identification of clusters in t-SNE embeddings. Finally, for datasets that cannot be loaded into the memory, we present out-of-core randomized principal component analysis (oocPCA), so that the top principal components of a dataset can be computed without ever fully loading the matrix, hence allowing for t-SNE of large datasets to be computed on resource-limited machines.