  The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm has become
in recent years one of the most used and insightful techniques for the
exploratory data analysis of high-dimensional data. tSNE reveals clusters of
high-dimensional data points at different scales while it requires only minimal
tuning of its parameters. Despite these advantages, the computational
complexity of the algorithm limits its application to relatively small
datasets. To address this problem, several evolutions of tSNE have been
developed in recent years, mainly focusing on the scalability of the similarity
computations between data points. However, these contributions are insufficient
to achieve interactive rates when visualizing the evolution of the tSNE
embedding for large datasets. In this work, we present a novel approach to the
minimization of the tSNE objective function that heavily relies on modern
graphics hardware and has linear computational complexity. Our technique does
not only beat the state of the art, but can even be executed on the client side
in a browser. We propose to approximate the repulsion forces between data
points using adaptive-resolution textures that are drawn at every iteration
with WebGL. This approximation allows us to reformulate the tSNE minimization
problem as a series of tensor operation that are computed with TensorFlow.js, a
JavaScript library for scalable tensor computations.
