Nice to see PCA in an HN article, it's a very powerful tool. For those struggling to get the example in this article, I find PCA easier to understand given vis

Nice to see PCA in an HN article, it's a very powerful tool. For those struggling to get the example in this article, I find PCA easier to understand given visual examples, and in less dimensions (try http://en.wikipedia.org/wiki/File:GaussianScatterPCA.png http://en.wikipedia.org/wiki/File:GaussianScatterPCA.png) Note how this dataset is two dimensional in nature, and PCA yields two vectors. The first gives the direction of the greatest variation, and the next gives the variation orthogonally to the first. An awesome use of PCA is for facial detection, a method called 'Eigenfaces' http://en.wikipedia.org/wiki/Eigenface http://en.wikipedia.org/wiki/Eigenface