tSNEJS is an implementation of t-SNE visualization algorithm in Javascript.

t-SNE is a visualization algorithm that embeds things in 2 or 3 dimensions. If you have some data and you can measure their pairwise differences, t-SNE visualization can help you identify clusters in your data. See example below.

The main project website has a live example and more description.

The algorithm was originally described in this paper:

L.J.P. van der Maaten and G.E. Hinton.
Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research
9(Nov):2579-2605, 2008.

You can find the PDF here.

Import tsne.js into your document: <script src="tsne.js"></script> And then here is some example code:

var opt = {epsilon: 10}; // epsilon is learning rate (10 = default)
var tsne = new tsnejs.tSNE(opt); // create a tSNE instance

// initialize data. Here we have 3 points and some example pairwise dissimilarities
var dists = [[1.0, 0.1, 0.2], [0.1, 1.0, 0.3], [0.2, 0.1, 1.0]];
tsne.initDataDist(dists);

for(var k = 0; k < 500; k++) {
  tsne.step(); // every time you call this, solution gets better
}

var Y = tsne.getSolution(); // Y is an array of 2-D points that you can plot

The data can be passed to tSNEJS as a set of high-dimensional points using the tsne.initDataRaw(X) function, where X is an array of arrays (high-dimensional points that need to be embedded). The algorithm computes the Gaussian kernel over these points and then finds the appropriate embedding.

Send questions to @karpathy.