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Basically find the primary eigenvectors.


It's not, though...

In sparse coding, you're generally using an over-complete set of vectors which decompose the data into sparse activations.

So, if you have a dataset of hundred dimensional vectors, you want to find a set of vectors where each vector is well described as a combination of ~4 of the "basis" vectors.




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