

Fast Randomized SVD (2014) - Cynddl
https://research.facebook.com/blog/294071574113354/fast-randomized-svd

======
Cynddl
Posted six months ago on
[https://news.ycombinator.com/item?id=8525237](https://news.ycombinator.com/item?id=8525237).

> We will soon release the implementations for these algorithms described.

I would like to see them now.

~~~
ajtulloch
See [https://github.com/facebook/fbpca](https://github.com/facebook/fbpca)

~~~
Cynddl
Thanks!

------
rwitten
If you're interested in lower bounds or tighter upper bounds, you can find the
latest here:

[http://link.springer.com/article/10.1007%2Fs00453-014-9891-7...](http://link.springer.com/article/10.1007%2Fs00453-014-9891-7#page-1)
[http://statweb.stanford.edu/~candes/papers/RandomizedNLA.pdf](http://statweb.stanford.edu/~candes/papers/RandomizedNLA.pdf)

------
inglor
Why aren't they just using compressed sensing instead of PCA in the first
place? PCA is good because it guarantees perfect recovery wen the set of
examples is contained in an n dimentional subspace. Compressed sensing
guarantees recovery whenever the set of examples is sparse in _some basis_ \-
it sounds like a much better fit.

Not to mention random projections which are even faster (even proved by the
Johnson-Lindenstrauss lemma) usually do well,

~~~
alphaBetaGamma
How does compressed sensing work if you don't know the base in which the
signal is sparse beforehand?

~~~
inglor
Start at slide 44
[http://www.cs.huji.ac.il/~shais/Lectures2014/lecture11.pdf](http://www.cs.huji.ac.il/~shais/Lectures2014/lecture11.pdf)

