I've been playing with the tensorflow standalone embedding projector (http://projector.tensorflow.org/) and found it a very helpful tool for visualization. However, when I try to replicate the t-sne result using other implementations (e.g., Rtsne, sklearn.manifold.tsne), the low dimension projection seems to be very different. Particularly, the clusters are much more spread-out in the embedding projector than that learnt using R or python packages.
I used the same perplexity, learning rate and momentum parameters. And tried both spherizing or not spherizing the data as implied in the projector.
Could anyone help to shed light on the difference between the tensorflow projector implementation of the t-sne algorithm and other implementations like Rtsne? For example, is there a similar 'exaggeration' parameter used in the projector as in Rtsne? What is the optimization algorithm? Or is there anything special in generating the visualization?
I believe the source code of the tensorflow projector is the oss_demo_bin.js file in https://github.com/tensorflow/embedding-projector-standalone. Unfortunately I'm not familiar with javascript and found it hard to interpret.
Thanks!
