DOSNES

DOSNES is a new method to visualize your data.

Project Page

http://yaolubrain.github.io/dosnes/

Paper

Doubly Stochastic Neighbor Embedding on Spheres <br> Yao Lu, Jukka Corander, Zhirong Yang <br> Pattern Recognition Letters, 2019

How to use?

Here is a simple example.

% Generate data and its similarity matrix
X = randn(1000,10);
D = pdist2(X,X,'squaredeuclidean');
P = exp(-D);

% Normalize the similarity matrix to be doubly stochastic by Sinkhorn-Knopp method
for i = 1:100
    P = bsxfun(@rdivide,P,sum(P,1));
    P = bsxfun(@rdivide,P,sum(P,2));
end    

% Run t-SNE with the spherical constraint
Y = tsne_p_sphere(P);

% Normalize Y to have unity radius for visualization
Y = bsxfun(@rdivide,Y,sqrt(sum(Y.^2,2)));

% Save the data 
dosnes_data = [Y ones(length(Y),1) 5*ones(length(Y),1)];
csvwrite('data.csv',dosnes_data);

Now open dosnes.html with Firefox. Don't use Chrome. You now have the DOSNES visualization in your browser.

<img src="img/dosnes_example.png" width="500">

For visualizations of more features and real world data, please see the demo folder.

In main javascript code of the demos, makeTextSprite() is to create the facing-to-viewer text labels. In the CSV files, the first three columns are XYZ-coordinates, the forth column is the class label and the last column is the size of the data points.

Python Implementation

https://github.com/Coni63/DOSNES