text: Title: Manifold Feature Engineering
  Body: ![Manifold Methods](http://scikit-learn.org/stable/_images/sphx_glr_plot_compare_methods_001.png) 
  
  Currently we have a t-SNE visualizer for text, but we can create a general manifold learning visualizer for projecting high dimensional data into 2 dimensions that respect non-linear effects (unlike our current decomposition methods. 
  
  ### Proposal/Issue
  
  The visualizer would take as hyperparameters:
  
  - The color space of the original data (either by class or more specifically for each point) 
  - The manifold method (string or estimator) 
  
  It would be fit to training data. 
  
  The visualizer would display the representation in 2D space, as well as the training time and any other associated metrics. 
  
  ### Code Snippet
  
  - Code snippet found here: [Plot Compare Manifold Methods](http://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods.html)
  
  ### Background
  
  - [Comparison of Manifold Algorithms (sklearn docs)](http://scikit-learn.org/stable/modules/manifold.html#manifold)
  
  This investigation started with self-organizing maps (SOMS) visualization:
  
  - http://blog.yhat.com/posts/self-organizing-maps-2.html
  - https://stats.stackexchange.com/questions/210446/how-does-one-visualize-the-self-organizing-map-of-n-dimensional-data
  