We're going to compare some different techniques that reduce the dimensionality of our data so we can visualize it. We'll go through each one step by step including the math and I'll answer questions along the way. And I freestyle.
Code for this video:
https://github.com/llSourcell/How_to_Simplify_Your_Data-LIVE-
Links from the video:
https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/
http://setosa.io/ev/eigenvectors-and-eigenvalues/
More learning resources:
https://plot.ly/ipython-notebooks/principal-component-analysis/
http://sebastianraschka.com/Articles/2014_pca_step_by_step.html
https://www.quora.com/What-is-the-difference-between-LDA-and-PCA-for-dimension-reduction
https://www.quora.com/What-advantages-the-t-sne-algorithm-has-over-pca
http://stats.stackexchange.com/questions/123040/whats-wrong-with-t-sne-vs-pca-for-dimensional-reduction-using-r
https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm
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