klotz: pca*

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  1. PC’s are a series of linear least squares fits to a sample, each orthogonal to all previous
    2013-08-23 Tags: , by klotz
  2. 2014-12-01 Tags: , by klotz
  3. I used a Python t-SNE library to reduce the 200 feature dimensions for each word to 2 dimensions and plotted them in matplotlib. I saved out the x/y coordinates for each word in the book, so that I can show those words on the graph as you mouse over the replaced (blue) words.
  4. In your example if you use PCA to initialize your t-SNE you get widely spaced centroids; if you use random initialization you'll get tiny centroids and an uninteresting picture.

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