Principal Component Analysis Explained Visually (2015)

Nice visualization! This provides me an opportunity to go on a random tangent on PCA: The post considers PCA from visualization perspective, but the exactly same thing can also be viewed as a method for reducing number of dimensions in the original dataset. [1] Now, one of the interesting questions in a dimensionality reduction task is, how to pick the number of dimensions (principal components)? A good number? In a principled way, instead of just computing the next component and…

Here is the best PCA explanation I ever read on the web: https://stats.stackexchange.com/questions/2691/making-sense-...

Seeing how each visualization adjusts as I change the original dataset is so useful. The technique reminds me of Bret Victor's amazing work. Ladder of Abstraction Essay: http://worrydream.com/#!2/LadderOfAbstraction Stop Drawing Dead Fish Video: https://vimeo.com/64895205 This is awesome, thanks for sharing!

Here is a much better explanation of PCA: https://stats.stackexchange.com/questions/2691/making-sense-... The key insight that many are missing is that PCA solves a series of optimization problems, namely that reconstructing the data from the first k PCs gives the best k-dimensional approximation in terms of the squared error. Even more, this is equivalent to assuming that the data lives in a k-dimensional subspace and becomes truly high-dimensional because of normally distributed noise that spills into every…