SVD
Singular value decomposition (SVD) is a method of breaking apart matrices that has many applications in statistics and scientific research. It also happens to pe fairly good as a lossy data compression method.
I've written a little bit about how this process works in a blog post. The first image presented here is what happens when you take one feature from each color channel of an image and pass it back out to an image library. Flip through the images below to see how quickly objects in the image start to take shape!
My original experiment was 100 features. Already at this scale, there's very little data missing that would lead people to believe this wasn't the original image. The approximate SVD for the image above using 128 features for each color channel is still only 12% the data size of the original pixel information.
