t-SNE is a dimension reduction technique that is particularly good for visualizing high dimensional data. This is an attempt to implement this algorithm using Spark to leverage distributed computing power.
The project is still in progress of replicating reference implementations from the original papers. Spark specific optimizations will be the next goal once the correctness is verified.
Currently I'm showcasing this using the standard MNIST handwriting recognition dataset. I have created a WebGL player (built using pixi.js) to visualize the inner workings as well as the final results of t-SNE. If a WebGL is unavailable for you, you may checkout the d3.js player instead.
- t-SNE Julia implementation
- Barnes-Hut t-SNE
- Web demo by Andrej, here.