title: tensorflow embedding projector t-sne algorithm difference from other implementation?
body: <p>I've been playing with the tensorflow standalone embedding projector (<a href="http://projector.tensorflow.org/" rel="noreferrer">http://projector.tensorflow.org/</a>) 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. </p>
  
  <p>I used the same perplexity, learning rate and momentum parameters. And tried both spherizing or not spherizing the data as implied in the projector. </p>
  
  <p>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?</p>
  
  <p>I believe the source code of the tensorflow projector is the oss_demo_bin.js file in <a href="https://github.com/tensorflow/embedding-projector-standalone" rel="noreferrer">https://github.com/tensorflow/embedding-projector-standalone</a>. Unfortunately I'm not familiar with javascript and found it hard to interpret. </p>
  
  <p>Thanks!</p>